A central challenge for articulatory speech synthesis is the simulation of realistic articulatory movements, which is critical for the generation of highly natural and intelligible speech. This includes modeling coarticulation, i.e., the context-dependent variation of the articulatory and acoustic realization of phonemes, especially of consonants. Here we propose a method to simulate the context-sensitive articulation of consonants in consonant-vowel syllables. To achieve this, the vocal tract target shape of a consonant in the context of a given vowel is derived as the weighted average of three measured and acoustically-optimized reference vocal tract shapes for that consonant in the context of the corner vowels /a/, /i/, and /u/. The weights are determined by mapping the target shape of the given context vowel into the vowel subspace spanned by the corner vowels. The model was applied for the synthesis of consonant-vowel syllables with the consonants /b/, /d/, /g/, /l/, /r/, /m/, /n/ in all combinations with the eight long German vowels. In a perception test, the mean recognition rate for the consonants in the isolated syllables was 82.4%. This demonstrates the potential of the approach for highly intelligible articulatory speech synthesis.
Citation: Birkholz P (2013) Modeling Consonant-Vowel Coarticulation for Articulatory Speech Synthesis. PLoS ONE 8(4): e60603. doi:10.1371/journal.pone.0060603
Editor: Francesco Pappalardo, University of Catania, Italy
Received: December 14, 2012; Accepted: February 28, 2013; Published: April 16, 2013
Copyright: © 2013 Peter Birkholz. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This study was funded by the University Hospital Aachen, Germany. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The author has declared that no competing interests exist.
Although established speech synthesis techniques like unit-selection synthesis  or statistical parametric speech synthesis  are now able to generate natural-sounding speech, in the longer-term, articulatory speech synthesis is still widely considered as the ultimate solution to speech synthesis . It is potentially much more flexible than the established techniques with respect to the simulation of specific voices, speaking styles, and emotions. In practice, however, it has proven to be difficult to generate natural-sounding speech even for a single voice and speaking style. The reason is that articulatory speech synthesis is an exceedingly complex task that requires the integration of elaborate models of the vocal tract (e.g. –), the vocal folds , the aero-acoustic simulation (e.g. , ), and articulatory control (e.g. –). The quality of the synthesis critically depends on the detail and realism of each individual model and their interplay.
The effective control of the model articulators is one of the major challenges in articulatory speech synthesis. A central difficulty is that speech sounds are coarticulated, i.e., the articulatory and acoustic realization of phonemes depends on the context . For example, the consonant /g/ in the syllable /gu/ is articulated with a more retracted tongue body and with more rounded lips than in the syllable /gi/. In unit-selection speech synthesis, such coarticulatory variations are captured by concatenating natural speech units from a large database with realizations of each phoneme in a variety of contexts. However, for articulatory speech synthesis it is not practical to record articulatory data of phonemes in all possible phonetic contexts, because articulatory recordings and their analysis are much more intricate than acoustic recordings. Instead, articulatory speech synthesis depends on numerical models of coarticulation.
One of the earliest coarticulation models was presented by Öhman . Here, the time-varying vocal tract shape (in terms of the vocal tract area function) in vowel-consonant-vowel (VCV) utterances was modeled as the superposition of a diphthongal vowel gesture spanning the two vowels and a consonant gesture. The influence of the vowel shape on an assumed ideal consonant target shape was modeled with a context-independent coarticulation function, which specifies the amount to which the ideal consonant shape is allowed to be distorted by the vowel shape as a function of the position along the vocal tract center line. For an alveolar stop, for example, the allowed distortion is small in the region of the tongue tip, but higher in the region of the tongue back. Öhmans work led to a number of vocal tract area function models based on the superposition principle to simulate coarticulation –. Birkholz et al. ,  simulated coarticulation with a 3D articulatory model of the vocal tract based on a dominance model, which can be considered as an extension of Öhmans approach from the domain of the vocal tract area function to the articulatory domain. Here, each vocal tract parameter of a consonant is associated with a context-independent dominance value, which specifies the degree of involvement of the parameter in the formation of a closure or constriction. The less involved a parameter is, the more it is determined by the underlying vowel. Lindblom and Sussman  recently used a similar approach with a 2D articulatory model to analyze the cause of locus equations .
Fowler and Saltzman  consider the concept of coarticulation in the context of a task-dynamic model. In this model, the articulatory and acoustic context-sensitivity of phonemes arises primarily from the dynamic interaction of gestures, as defined in articulatory phonology . In the utterance /da/, for example, a gesture for the realization of /d/ would be assumed to overlap in time with the vowel gesture for /a/, both competing for the control of the shared articulators tongue body and jaw. Task-dynamics involves a system to blend the influences of the competing gestures on the articulators based on the interaction of dynamical systems. The concept of gestures was also adopted by Kröger  in the context of a speech production model of German. More recently, cognitive models of speech production and perception were proposed with the aim to learn the context-dependent coordination of articulatory movements from acoustic training data –.
Most of the past models aimed at the simulation of basic articulatory phenomena without the focus on perception or high-quality articulatory speech synthesis. This paper presents a novel approach to simulate consonant-vowel coarticulation effectively both at the articulatory and the acoustic level. Therefore, consonants were modeled in terms of context-sensitive articulatory targets . The basic idea was to calculate the context-sensitive target of a consonant as the weighted average of reference targets of the consonant in the context of the corner vowels /a/, /i/ and /u/, i.e., by bilinear interpolation. By definition, the corner vowels represent the most extreme vowel articulations in terms of the tongue position, i.e., /a/ is produced with the tongue as low and as far back as possible, /i/ with the tongue as high and forward as possible, and /u/ with the tongue as high and as far back as possible, while all other vowels are assumed to be produced with tongue positions in between these. The corner vowels also mark the corners of the acoustic vowel space. Our assumption in this study was that the different realizations of a consonant in the context of these corner vowels accordingly reflect the extremes of its coarticulatory and acoustic variation. The reference targets for consonants, i.e., their three articulatory realizations in the context of /a/, /i/, and /u/, were obtained from real-time MRI scans and optimized with respect to their acoustic realization. The weights for averaging the reference targets were determined by mapping the actual context vowel of the consonant into the articulatory subspace spanned by the corner vowels /a/, /i/, and /u/. In this study we considered the consonants /b/, /d/, /g/, /l/, /r/, /m/ and /n/, but the extension to other consonants is straightforward. The performance of the model was evaluated in terms of the recognition rate of the consonants in CV syllables synthesized with the articulatory speech synthesizer VocalTractLab (www.vocaltractlab.de).
The vocal tract and articulations modeled in this study are based on a native speaker of German. The following section describes the data analyzed from this reference speaker. Then we describe how this data was used to create the vocal tract model and the proposed coarticulation model. Finally, we turn to the perceptual experiment using synthesized CV-syllables and discuss the results.
All data used for modeling the vocal tract and articulation in this study were collected from the same adult native speaker of Standard German. Three types of data were analyzed: a corpus of volumetric MRI data of sustained phonemes, a corpus of real-time MRI data of CV syllables, and a corpus of high-quality audio data. Please note that due to font regulations of the journal, the ASCII-based phonetic alphabet SAMPA is used to refer to the different speech sounds . Figure 1 shows the SAMPA symbols used in this paper and their corresponding symbols in the International Phonetic Alphabet (IPA).
Volumetric MRI data and CT data
The volumetric MRI data of the vocal tract was available from a previous study  and consisted of sustained articulations of German vowels and consonants. This data was used to define the vocal tract model and to adjust vocal tract target shapes for vowels.
The images were acquired using a Philips Gyroscan NT scanner at the Institute for Radiology at the Virchow Clinical Center in Berlin. Each phoneme was recorded with 18 sagittal slices of 3.5 mm thickness and 512×512 pixels with a pixel size of 0.59×0.59 . The acquisition took 21 s per phoneme. In the present study, we analyzed the data for the long vowels /a:/, /e:/, /i:/, /o:/, /u:/, /E:/, /2:/ and /y:/, and the short vowels /I/, /E/, /a/, /O/, /U/, /Y/, /9/, /@/ and /6/.
For each of these phonemes, the vocal tract contours in the midsagittal slice were manually traced using Catmull-Rom splines. To highlight the edges in the MR images for tracing, the Sobel operator was applied to the original images. The tongue outline was not only traced in the midsagittal slice, but also in the slice about 10 mm to the left of the middle. The two outlines of the tongue were later used to reproduce the cross-sectional shape of the tongue. Finally, the mandible bone was traced in each midsagittal image as an indicator for the degree of jaw opening. Figure 2A shows as an example the midsagittal slice of the vowel /y:/, the highlighted edges, and the traced contours. Besides the sagittal contours, we measured the lateral width of the larynx and pharynx at multiple positions for phonemes with a fronted tongue position as estimates of the lateral dimensions of the vocal tract in these regions.
A: Original midsagittal image of the vocal tract for the vowel /y:/ from the volumetric MRI corpus (left), the same image with enhanced edges (middle), and the traced contours (right). B: Same as A for an image of the real-time MRI corpus showing the consonant /d/ in /a/-context. The thick dashed lines in the traced images show the outline of the tongue side. The thin dashed lines indicate the angle of the rear pharyngeal wall with respect to the hard palate, which varies between the two corpora. The traced images were rotated for an identical orientation of the hard palate. (Figure modified from ).
In addition to the vowels, we analyzed the consonants /s/ and /m/ with respect to the shape of the velum (note that /s/ is not part of the actual coarticulation study). This data was later used to define reference shapes for the maximally raised and lowered velum of the vocal tract model. Finally, plaster models of the hard palate and the mandible (including all teeth) of the speaker were scanned in a computer tomograph with a voxel size of 0.226×1×0.226 . This data was used for modeling the corresponding rigid 3D structures in the vocal tract model.
Midsagittal real-time MRI data
The real-time MRI data of the vocal tract was also available from a previous study  and consisted of sequences of the pseudowords /baCa/, /biCi/ and /buCu/ with different consonants C. This data was used to reproduce the context-sensitive reference shapes of the vocal tract for the consonants /b/, /d/, /g/, /l/, /r/, /m/ and /n/. Therefore, we analyzed the MRI sequences with the consonants /b/, /d/, /g/, /l/, and /x/. The velar/uvular voiceless fricative /x/ in the corpus was used as substitute to model the uvular approximant /r/, because the corpus did not contain recordings of /r/. Furthermore, the consonants /m/ and /n/ were modeled using the data for /b/ and /d/ with a lowered velum, as described further below.
The data was acquired for midsagittal sections of the vocal tract at a frame rate of 8 Hz using a Philips Gyroscan NT scanner at the Department of Radiology at the Technical University of Munich. The slice thickness was 10 mm and the resolution was 256×256 pixels with a pixel size of 1.18×1.18 . The pseudowords were produced consecutively at a normal speaking rate and repeated about ten times each. From this data, we selected the images showing the consonants /b/, /d/, /g/, /l/, and /x/ in the context of the vowels /a/, /i/ and /u/ as basis for the proposed coarticulation model. Because of the low frame rate of 8 Hz, the consonants were sampled in only some of the repeated pseudowords during the interval of the corresponding consonantal constriction. Therefore, we first visually identified the sets of frames that actually represented the context-sensitive consonantal targets, and then selected the most representative frame from each set. The most representative frame was taken to be the one with the smallest “distance” to all other images in the set (in terms of signal energy in the difference image), i.e., the most central member of the set. For example, in the 10 repetitions of /bada/ we identified three frames with a satisfactory alveolar closure for /d/. From these three frames, one frame was selected as a template for /d/ in /a/-context. In the selected frames the vocal tract contours were traced analogous to the volumetric images. Because this corpus consisted only of midsagittal slices, it contained no information about the contours of the tongue side. This information was complemented with measurements from a recent pilot study (unpublished data) where the same VCV sequences were recorded with a new real-time MRI technique  where two parallel sagittal slices (in the middle and 1 cm to the left) were recorded simultaneously at a frame rate of 25 Hz. Because the speaker in the pilot study was not the one modeled in this study, the contours of the tongue side must be considered as approximations. Figure 2B shows an example of a real-time MRI frame for /d/ in /a/-context and the corresponding edge and contour images.
Normalization of head posture
As Figure 2 illustrates, the head posture, i.e., the angle of the rear pharyngeal wall with respect to the hard palate, was not identical in both MRI corpora. Also within each corpus, the postures varied slightly. To merge both corpora for modeling, we normalized the head postures in all MRI tracings as previously described in . Basically, we assumed the oral and pharyngeal parts of the vocal tract to be connected as if by a hinge joint, where different postures correspond to different hinge angles. The position of the fulcrum was determined as the point where the straight line approximations of the rear pharyngeal wall of the contour tracings from the two corpora intersected. Because all the different straight line approximations did not exactly intersect at the same position, the common fulcrum position was determined in a least-square sense . After that, each MRI tracing was warped such that the rear pharyngeal outline was oriented at a pre-defined constant angle. Warping was implemented using the method by Beier and Neely  with three corresponding pairs of vectors, as illustrated in Figure 3. A horizontal vector on top of the palate and a vertical vector at the chin were identical in both the original and the warped image to preserve the vocal tract shape in these regions of the vocal tract. The third vector was aligned to the rear pharyngeal wall in the original image and rotated around the fulcrum to assume the predefined angle in the warped image.
The source shape has a dotted outline and the target shape a solid outline. Three pairs of corresponding vectors were used to define the warping. The superior and anterior vectors were identical for the source and the target shapes, keeping these parts of the vocal tract essentially equal. The posterior vector was aligned with the rear pharyngeal wall in the source shape and rotated to the required orientation for the (normalized) target shape around a common fulcrum.
The purpose of the audio recordings was to obtain a complete set of speaker-specific formant frequency targets for the German vowels and for the formant frequencies at the onset of the vowels after the consonants /b/, /d/, /g/, /l/, /r/. The consonants /m/ and /n/ were not considered in this audio corpus, because their antiresonances prevent reliable formant measurements. Because of the high noise level in the MRI scanner, these recordings were made in a separate session in a sound-proofed room for a high audio quality. To have the same postural influence on articulation as in the MRI scanner, the recordings were made with the speaker in supine position. The speech was recorded with a 44 kHz sampling rate at 16 bit quantization to a digital tape recorder using a high-quality microphone mounted on a headset.
The speaker read a number of target words (prompts), each of which was spoken in the carrier sentence “Ich habe ... gesagt. ” at a comfortable speed, pitch and loudness. The prompt list consisted of the nonsense words /CVd@/ for all combinations of the consonants /b/, /d/, /g/, /l/, /r/ and the long vowels /a:/, /e:/, /i:/, /o:/, /u:/, /E:/, /2:/, /y:/, the words /CVt@/ for all combinations of the same consonants and the short vowels /I/, /E/, /a/, /O/, /U/, /Y/, /9/, as well as the words /hOp6/, /hOk6/, and /mUt6/ with the low Schwa as final vowel. This prompt list with a total of 78 items was recorded six times (three times in each of two separate sessions) for six instances of each target word in total.
In the words /CVd@/ and /CVt@/, the first three formant frequencies , , and were measured at the onset of voicing of the target vowel, in the middle of the target vowel, and for the final Schwa. In addition, the voice-onset time (VOT) was determined in the words with initial plosives, measured from the closure release to the middle of the first fully established glottal period of the vowel. In the words /hOp6/, /hOk6/, and /mUt6/, only the formants of the final low Schwa /6/ were measured.
The formants were determined manually using the software Praat version 5.1.18 based on the built-in LPC formant tracker. For each word, the number of LPC poles was carefully adjusted for the best possible visual match between the peaks in the wideband spectrogram display and the superimposed LPC-based estimation. Formant frequencies at vowel onset after the initial consonants were measured at the first discernible glottal pulse after the release burst or frication phase. For the fully voiced /l/, the “onset formants” were measured in the stationary phase of the lateral. The formants of vowels were measured as mean values in the visually-determined steady state portions of the vowel. When a formant trajectory was diagonally rising or falling, the target value was taken at the midpoint of the vowel. For a U-shaped formant trajectory in the vowel portion (or the inverse), the minimum (or maximum) frequency was taken as target. About 1% of the formant values could not be uniquely identified or measured and were excluded from the subsequent analysis.
The measurement results are presented in Figure 4, 5, and Table 1. Figure 4 shows the distributions for the analyzed vowels in terms of the convex hulls around the respective vowel samples. Each region was determined from 30 samples (each vowel after five different consonants with six repetitions). The regions for the long vowels are painted in gray.
The gray and white regions surround the long and short vowels, respectively. For each vowel, the mean formant frequencies of the analyzed samples were taken as the underlying acoustic target for the vowel according to the undershoot model . Exceptions were the vowels /2:/ and /a:/, for which the mean formant values (white squares) did not represent perceptually high-quality targets. Instead, the acoustic targets for these vowels were obtained from additionally recorded sustained /2:/ and /a:/ (white circles).
Panel A shows the transitions for /b/, /d/, and /g/, and panel B for /l/ and /r/.
Figure 5 shows , , and of the consonants in relation to the formant targets of the vowels. The shown formant onset values are the median values (because of the small sample size) of the six measured instances of each CV-combination, and the formant target values are the mean values of the 30 instances per vowel. The connecting lines represent the stylized formant transitions from the consonants to the vowels and illustrate the acoustic variability of the consonants.
Table 1 contains the numerical values for the measured formants. As in Figure 5, formant targets for vowels are mean values and formant onset values are median values. The only exceptions are the formant targets for the vowels /a:/ and /2:/, for which the mean values of the measured instances did not represent a perceptually high-quality target for the vowel according to informal listening tests using a formant synthesizer. Instead, the data for /a:/ and /2:/ in Table 1 represent the formants of additional recordings of sustained articulations of these vowels (shown by white circles in Figure 4). In general, it seems that the mean formant values of a vowel measured in different contexts does not necessarily represent an ideal target for the vowel.
Vocal tract model
Using the acquired speaker data, a geometrical 3D vocal tract model was developed to represent the time-varying shape of the supraglottal airways. This 3D shape is the basis for accurate calculation of area functions of the vocal tract for the acoustic simulation. The current model is an extension of our previous vocal tract model, which was presented in detail in , , . This section gives a brief overview of the model and highlights the improvements since the previous version, in particular the control of the jaw and the velum.
Generally, the vocal tract is defined in terms of a number of geometric surfaces of the articulators and vocal tract walls as illustrated in Figure 6. Their shape and position in 3D space are specified by a set of control parameters summarized in Table 2, each corresponding to one degree of freedom (DOF). The parameters were carefully defined to permit the flexibility needed to produce a large set of speech sounds, while as far as possible prohibiting anatomically impossible shapes. This was supported by geometrical constraints that, e.g., prevent interpenetrations of the articulators.
A: Rendering of the vocal tract model for the vowel /E:/. B: Wireframe representation of the model surfaces. C: Area function of the vocal tract shape in panel A used for the acoustic simulation. The area function describes the acoustically relevant variation of the cross-sectional area of the vocal tract between the glottis (at 0 cm) and the mouth opening (here at 15.5 cm).
The posterior-superior cover surface defines the shape of the hard palate, the velum, and the posterior wall of the pharynx and larynx. The anterior-inferior cover defines the shape of the anterior parts of the larynx and pharynx and the jaw. The remaining surfaces define the shapes of the tongue, lips, upper and lower teeth, uvula and epiglottis. The surfaces for the rigid parts of the vocal tract, i.e., the hard palate, the jaw, and the teeth, were closely adapted to the reference speakers' geometry using the CT data of the plaster models of these parts.
While the hard palate has a fixed position in the 3D coordinate system of the vocal tract, the jaw is assumed to execute rotational and translational movements controlled by the parameters JA and JX. JA defines the angle of rotation of an angle bracket around a transverse axis, as illustrated in Figure 7. The jaw is assumed to slide along the long lever of the angle bracket, and JX defines the displacement of this translational movement. The location of the fulcrum was estimated to model the dependency between the opening and the rotation of the jaw observed in the static MRI data. Note that it is more posterior than the actual anatomical mandibular joint. Thereby, a rotation around the fulcrum corresponds to a combined rotation and vertical translation of the jaw with respect to the real temporomandibular joint .
The shape of the velum is controlled by VO and VS. The protrusion of the lips and the vertical distance between the upper and lower lip is specified by LP and LD, respectively. The horizontal and vertical position of the hyoid is specified by HX and HY, respectively. JA specifies the opening angle of the jaw and the JX its anterior-posterior translation. The tongue body and the tongue tip are modeled as circles with the center coordinates (TCX, TCY) and (TTX, TTY), respectively. The tongue root and the tongue blade are modeled with quadratic Bézier curves. The coordinates of the central control points of these curves are given by (TRX, TRY) and (TBX, TBY). The panel at the bottom illustrates the cross-section of the tongue at a position along the midsagittal tongue contour. h(t) defines the elevation of the tongue sides, which is specified by the parameters TS1 ... TS4 at four equally-spaced positions along the tongue contour. Between these positions, h(t) is interpolated.
The velum shape was traditionally modeled by linear interpolation between two extreme positions: the highest possible position corresponding to a closed velo-pharyngeal port, and the lowest possible position corresponding to a maximally open port (e.g. , , ). However, the relation between the height of the velum and the acoustically-important velar opening area is difficult to determine with only one DOF for the velum. For example, our data showed considerable variations of the velum height for different vowels, all of which can be assumed to be produced with an essentially closed velo-pharyngeal port. A recent study actually found two independent DOF of the velum, both of which affect the velum shape and the velar opening area . Therefore, we decided to model the function of the velum with two control parameters instead of one, namely the velum shape parameter VS and the velic opening parameter VO. VS defines the shape of the velum for a closed velo-pharyngeal port (VO = 0) by linear interpolation between a maximally raised position as in /s/, and a lowered position as in /a/. VO interpolates the final velum shape between the closed-port shape specified by VS and a maximally lowered shape as in /m/. The three reference shapes were modeled after the volumetric MR images of /s/ (VS = 0 and VO = 0), /a/ (VS = 1 and VO = 0), and /m/ (VO = 1), and are shown in Figure 7 as black, gray, and light gray contours, respectively. The velic area is assumed to be with negative areas being set to zero.
Two parameters, HX and HY, define the position of the hyoid and also determine the shape of the larynx, similar to Mermelsteins model . HY defines the absolute vertical position of the hyoid and the larynx below. The actual shape of the larynx is linearly interpolated between two reference shapes for the narrowest (HX = 0) and the widest (HX = 1) larynx shapes observed in the volumetric MRI data. The shape of the lips is defined by the parameters LP and LD, which define the protrusion of the lip corners and the vertical distance between the upper and lower lip. From these parameters we derive all other important lip dimensions according to  and so construct the lip surfaces.
The 3D shape of the tongue is defined in terms of its midsagittal shape and the height of the tongue sides. The tongue body is represented by a circle with a fixed radius and a moving center defined by the absolute coordinates (TCX,TCY). The tongue tip is represented by a smaller second circle and the variable center coordinates (TTX,TTY). The root of the tongue is modeled with a quadratic Bézier curve defined by three control points. The first point is given by the hyoid position, the second point by the control parameters (TRX,TRY), and the third is the contact point of the tangent line to the tongue body circle that runs through (TRX,TRY). The tongue blade is also modeled with a quadratic Bézier curve where the second control point position is defined by the parameters (TBX,TBY), and the first and the third points are the contact points of the tangent lines to the tongue body circle and the tongue tip circle that run through (TBX,TBY). The two circles and the control polygons for the two splines are shown with dashed lined in Figure 7. The parameters TS1, TS2, TS3, and TS4 define the height of the tongue sides with respect to the midsagittal contour at four equally-spaced positions between the hyoid and the tongue tip. This allows modeling of varying degrees of convex and concave cross-sections of the tongue surface along the midsagittal contour.
The acoustic properties of the vocal tract are essentially determined by the area function, i.e., the variation of the cross-sectional area as a function of the position between the glottis and the lips. The area function is determined by intersecting the vocal tract surfaces with planes perpendicular to the center line of the airway according to . The calculation of the course of the center line is based on the position of the tongue body circle to dynamically adapt to the major shape variations of the vocal tract. Figure 6C shows as an example the area function for the vocal tract shape in Figure 6A. Because of the triangle mesh representation of the vocal tract surfaces, the precise cross-sectional areas in constricted regions of the vocal tract are sometimes difficult to control with the control parameters. However, for fricatives, the area of the constriction is a sensitive aerodynamic parameter for the flow resistance and the properties of the noise sources. To precisely control the cross-sectional area of constrictions, we introduced the parameters MA1, MA2 and MA3, which define the minimal area in the regions upstream from the tongue tip, in the near vicinity of the tongue tip, and in the region of the incisors and lips, respectively. These parameters directly affect the area function, where they ensure the adjusted minimal areas in the corresponding regions.
Modeling consonant-vowel coarticulation
To synthesize a sequence of phonemes with the model described above, the temporal variation of the vocal tract shape must be specified. In this study, we focus on the synthesis of consonant-vowel syllables as the most common and universal type of syllable in the world's languages . Here, a CV syllable is modeled as a smooth unidirectional movement from an initial vocal tract shape appropriate for the consonant to a target shape for the vowel. The target shape for the vowel is assumed to be invariant, i.e., independent of the preceding consonant, as suggested in the undershoot model by Lindblom . Hence, all potential variations of a vowel are assumed to be caused by vowel target undershoot. All other coarticulatory influences of consonants on vowels are currently considered as truly allophonic variations and would have to be modeled with different target shapes for different allophones of the vowels. In contrast to the vowel in the CV syllable, the vocal tract shape for the consonant is assumed to be context-sensitive , i.e., to vary depending on the context vowel. The context-sensitive consonant shape is derived as the weighted average of three reference shapes for that consonant in the context of the corner vowels /a/, /i/, and /u/. Before this approach is detailed further below, we discuss the adjustment of the vocal tract parameters for the vowels and the consonants in /a/, /i/, and /u/-context by means of the MRI-derived vocal tract contours and their acoustic optimization.
Reference vocal tract shapes for vowels.
For each vowel analyzed in the volumetric MRI corpus, the vocal tract parameters were manually adjusted for the best possible visual match between the traced contours (including the tongue sides) and the vocal tract model contours. The flexibility of the vocal tract model allowed a fairly good match of the contours, as demonstrated in Figure 8 (red vs. gray contours).
Under ideal conditions, a close match between the vocal tract shapes of the reference speaker and the model would result in a close acoustic match. However, this was not always the case, because among other reasons, the vocal tract model is a non-perfect approximation of the real speaker's vocal tract, the accuracy of the traced contours in the MRI data was limited by the image resolution, and the artificially sustained vowels of the speaker in the MRI machine do not necessarily represent the ideal vowel targets. And in some regions of the vocal tract, even small deviations from the “correct” articulation can cause substantial acoustic changes . Therefore, after the visual registration of the vocal tract shapes, the acoustic match between the natural and synthetic vowels was optimized. The goal was to minimize the acoustic deviations of F1, F2 and F3 between the model and the reference speaker by slight changes of the vocal tract shapes in terms of the control parameters. The acoustic error was defined as the root-mean-square of the relative error between the model-derived formants F1, F2 and F3, and the speakers' formants F1′, F2′ and F3′ (according to Table 1):(1)
The model formants were determined from the volume-velocity transfer function of the vocal tract calculated in the frequency domain from the area function according to , . To calculate the formants as accurately as possible, we considered acoustic energy losses due to sound radiation, soft walls, and viscous friction, as well as inner-length corrections of the vocal tract tube sections .
The optimization was implemented as a greedy algorithm. In each optimization step, the vocal tract parameter was identified for which a small positive or negative incremental change of the value resulted in the biggest reduction of the acoustic error E. After the incremental change had been applied to the parameter, the procedure was repeated for the resulting vocal tract shape until no reduction of E was possible anymore. The incremental changes of the parameters were defined such that the vocal tract outline displaced by no more than 0.5 mm per step. For the parameter TTX, for example, the possible increments were +0.5 mm and −0.5 mm. Two constraints were implemented: (1) The model contour of the vocal tract was not allowed to deviate more than a preset threshold from the initial MRI-fitted contour to keep the vocal tract shape geometrically similar to the MRI tracings. (2) The cross-sectional area of the vocal tract was not allowed to fall below a preset threshold to prevent unrealistically narrow constrictions that would cause excessive pressure drops or turbulence noise during the synthesis of the vowels . For most vowels, the maximal contour displacement was set to 2 mm and the minimal area to . With these settings, the acoustic error reduced to below 5%, which is the just-discriminable change in the frequencies of the first and second formants . However, for /a:/, /u:/, /2:/ and /y:/, the maximal allowed contour displacement had to be increased to 4 mm, and for /u:/ and /y:/, the area threshold had to be reduced to to achieve similar low errors. Figure 9A displays the acoustic errors for the vowels before (full bars) and after optimization (dark bars). On average, the error was 9.9% before and 1.2% after optimization. The black contours in Figure 8 illustrate the optimized vocal tract shapes for /a:/, /i/, and /u:/.
A: Formant frequency deviations between modeled and recorded vowels. B: Onset formant frequency deviations between the modeled and measured consonants /b/, /d/, /g/, /l/ and /r/ in the context of the three corner vowels.
Reference vocal tract shapes for consonants.
The normalized vocal tract tracings of the dynamic MRI corpus were used to model the context-sensitive targets for the consonants /b/, /d/, /g/, /l/, /r/, /m/, and /n/, each in the context of the vowels /a/, /i/, and /u/. Therefore, the vocal tract parameters were manually adjusted for a visual match with the MRI tracings analogously to the vowels. For /b/, /d/, /g/ and /l/, the articulatory data was directly available from the corpus. To model /r/, which was not recorded in the corpus, the tracings of /x/ were used, because /x/ and /r/ have the same place of articulation and differ mainly in the manner of articulation. In fact, the dorsal German /r/ may well be realized as a velar or uvular fricative in voiceless contexts such as the word “trat”. The vocal tract shape for /r/ in /i/-context, for which there was no data in the corpus, was manually modeled based on the contours of /g/ in /i/-context with a more retracted tongue. /m/ and /n/ were finally modeled based on /b/ and /d/ with a lowered velum as detailed further below.
For each of the target shapes for /b/, /d/, and /g/, the vocal tract parameter(s) for the primary articulator were set to a virtual target, i.e., to a position that actually cannot be reached for the articulator. This allows for simulation of the high velocities of the primary articulators at the time of closure release in the framework of the target approximation model . For example, the tongue tip target for /d/ was set to a position above the hard palate, and the lip distance for /b/ was set to a negative value. The virtual articulator positions were adjusted such that the release of the closure happened about in the middle of the transition from the context-sensitive consonant target to the corresponding context vowel target. For example, for the shape of /d/ in the context of /a/, the virtual target for the tongue tip was placed 10 mm above the hard palate, which coincides with the distance between the tongue tip and the palate in /a/. Figure 10 illustrates the effect of the virtual tongue body target of /g/ in the context of /a/. Here, the closure is released about half-way between the /g/-target and the /a/-target (dashed tongue contour). For the three context-sensitive targets of /l/, the tongue tip was set to positions where it just touched the palate, i.e., to non-virtual targets. The tongue sides were lowered to create lateral channels with a total cross-sectional area of , which falls well in the range of measured by Narayanan et al. . The consonant /r/ was modeled as a voiced uvular fricative. For the three context-sensitive targets, the tongue body was set to a position where it just touched the velum. The control parameter MA1 was used to adjust the constriction area to , which is a typical minimal constriction area for voiced fricative consonants .
The solid vocal tract outlines show the vocal tract shapes for /g/ and /a/. The dashed contour shows the tongue shape at the time of closure release. , , and are the times of movement onset, closure release, and movement offset, respectively.
After the manual adjustment of the vocal tract contours, the consonant shapes were acoustically optimized similarly to the vowels. Here, the differences between the measured and synthesized formants at vowel onset (according to Table 1) were minimized, because these formant frequencies are important (although not the only) perceptual cues for the place of articulation , . For /b/, /d/, /g/, and /r/, vowel onset was assumed to happen at the point along the linear transition from the context-sensitive consonant shape to the corresponding vowel shape where the constriction area increased to a value of . To our knowledge, due to the limitations of the current measurement technology, there are no precise data about the constriction area of plosives or fricatives at the time of voice onset. Therefore, the area of was estimated based on the data of voice onset times and rates of constriction area increase after plosives , . For /l/, the formants were calculated directly from the consonant targets. For all consonant optimizations, the maximal allowed contour displacement was set to 4 mm instead of 2 mm as for the vowels, because of the greater uncertainties in the traced contours due to the low spatial and temporal resolution of the dynamic MRI data. The vocal tract parameters that defined the position of the primary articulator were not modified during the optimization.
The context-sensitive targets for /m/ and /n/ were modeled with the same optimized shapes as for /b/ and /d/ with the velic opening parameter VO set to 0.5 (corresponding to a velic opening area of ). In some cases, the cross-sectional area between the tongue back and the lowered velum became unrealistically small. Therefore, if this area became smaller than , the tongue body position was adjusted as little as possible to establish a minimal area of .
Figure 9B shows the formant errors at voice onset before and after the acoustic optimization of the consonants. The mean acoustic errors of the consonants after optimization are given in the first row of Table 3. Figure 11A–C shows the optimized vocal tract targets for the consonants /b/, /d/, and /g/ in the context of the three corner vowels.
A–C: Reference vocal tract shapes for /b/, /d/ and /g/ in the context of the corner vowels. The dashed lines indicate the contour of the tongue side. D: Calculated vocal tract targets for /b/, /d/ and /g/ in the context of the vowel /y/ according to the proposed coarticulation model.
Modeling context-sensitive consonants.
The basic assumption of our model is that the vowels /a/, /i/, and /u/ effectively represent the “corners” of the lingual vowel space, and that the measured consonant targets in the context of these vowels represent the corresponding extreme points of the consonants' lingual coarticulatory variation. The idea is to consider an arbitrary context vowel as a weighted average of the vowels /a/, /i/, and /u/, and derive a context-sensitive consonant target as the accordingly weighted average of the measured consonants in the contexts /a/, /i/, and /u/. However, vowels are not only distinguished by their lingual articulation, but also by the rounding of the lips. For example, /i/ and /y/ are characterized by roughly the same tongue position, but the lips are unrounded for /i/ and rounded for /y/. Therefore, the lip shape of a context-sensitive consonant is derived independently from the tongue shape based on the lip shape in the context vowel. Similar to the tongue shapes, the lip shapes of /a/, /i/ and /u/ can be roughly considered as extreme labial articulations for vowels in terms of lip protrusion and lip aperture (maximal aperture for /a/, maximal protrusion and minimal aperture for /u/, minimal protrusion for /i/).
As discussed before, the shape of the vocal tract is defined by the parameters given in Table 2. For the determination of a context-sensitive consonant target, these parameters are divided into two sets – one that represents the lingual articulation in terms of the vector , and one that represents the labial articulation in terms of the vector (hence, all vocal tract parameters except the lip parameters were considered to specify the “lingual” articulation). Let us first detail the calculation of the “lingual” parameters of a context-sensitive consonant. When the reference shapes for the vowels /a/, /i/, and /u/ are given by the vectors , , and , an arbitrary context vowel with the parameter vector is expressed as a linear combination of these reference shapes:(2)or re-arranged
(3)The coefficients and determine the position of in the subspace defined by the three reference vowels. Because is likely to be singular, we use singular value decomposition to find a pseudo-inverse to determine and :(4)
The coefficients are then used to obtain the context-sensitive consonant target as(5)where , , and are the known parameter vectors for the consonant articulations in /a/, /i/, and /u/-context. Before is actually calculated, the position in the subspace is limited to the triangular region spanned by the reference vowels by enforcing the conditions , , and .
Figure 11D illustrates the method by means of the vocal tract targets for /b/, /d/ and /g/ in the context of the vowel /y/. When /y/ is mapped into the /a, i, u/-subspace, we get , , and . The lip shape lies slightly outside the triangle of the reference lip shapes, because . Therefore, and are reduced by equal amounts to 0.08 and 0.92 to satisfy the condition. The coefficients yield the linear combination for the lingual articulation, and the combination for the labial articulation. In other words, the lingual articulation of /y/ is most similar to /i/, and the labial articulation most similar to /u/. Putting and in Eqs. 5 and 7 results in the consonant targets in Figure 11D.
To check the effectiveness of the method for the acoustic simulation of the consonants, the target shapes for /b/, /d/, /g/, /l/ and /r/ were calculated in the context of all long and short German vowels except the corner vowels. For each of these CV-combinations, the vocal tract shape at the release of the consonantal constriction was calculated according to the previous section to obtain the formant frequencies at the vowel onset. The mean errors between the simulated and measured onset formant frequencies according to Eq. 1 are shown in the second row of Table 3. For /b/ and /d/, the error is roughly the same as for the consonants in the optimized reference contexts. For /d/, /l/ and /r/, the error increased by only 5.4, 7.3 and 6.7%. Hence, the proposed vocal tract shape interpolation allows a good simulation of the context-sensitive consonant acoustics across context vowels.
In order to assess the performance of the proposed coarticulation model, we tested the intelligibility of the modeled consonants in synthesized consonant-vowel syllables with a perceptual identification experiment.
The stimuli consisted of CV-syllables for all combination of the modeled consonants /b/, /d/, /g/, /l/, /r/, /m/, /n/ and the long vowels /a:/, /e:/, /i:/, /o:/, /u:/, /E:/, /2:/, /y:/, i.e., a total of 56 items. The stimuli were created with the articulatory synthesizer VocalTractLab (www.vocaltractlab.de), where the proposed coarticulation model was implemented. The vocal apparatus in VocalTractLab was modeled as a branched acoustic tube system comprising the trachea, the glottis, and the pharyngeal, oral and nasal cavities . The cross-sectional area function of the pharyngeal and oral cavities was determined by the vocal tract model presented above. The trachea was modeled as a uniform tube of 14 cm length, and the nasal cavity was modeled in terms of its area function according to the data of Dang et al. . The glottis was represented by two tube sections, the geometry of which was determined by the glottal shape model proposed by Titze  and extended by Birkholz . A detailed aero-acoustic simulation method generated the speech signal based on the acoustic tube system , , .
The stimuli were generated by specifying time-functions for the control parameters of the vocal tract model and the glottis model as illustrated in Figure 10. The parameters of the vocal tract model were initialized with the values for the context-sensitive consonant target and started to simultaneously approach their vowel target values at a time . The transition was modeled as the step response of a critically damped sixth-order linear-system , which closely resembles the sigmoidal trajectory of natural goal-directed movements. The velocity of the vowel target approach was controlled by the time constant of the system. The time constant was individually estimated for each consonant by matching the formant transition durations of the synthetic syllables with naturally-spoken syllables. It was set to 7 ms for syllables with /l/, to 15 ms for syllables with /b/, /d/, /g/, and /r/, and to 25 ms for syllables with /m/ and /n/. A higher time constant causes a longer transition time and a slower velocity of the articulators than a lower time constant. Therefore, the simulated closure release of the nasals was generally slower than the release of the plosives, which is in agreement with measured data . For the syllable /ga/ in Figure 10, the vowel target was reached at about the time , and the closure release happened at the time . As discussed above, the release occurred about half-way between the (virtual) target for the consonant and the vowel target. The tongue contour at the time of the release is shown by dashed lines in the vocal tract images for /g/ and /a/.
The glottis model was controlled in terms of the degree of glottal abduction (pre-phonatory rest displacement of the inferior and superior edges of the vocal folds from the glottal midline at the level of the vocal processes), fundamental frequency , and subglottal pressure . The time functions for and were adjusted identically for all stimuli. was quickly raised from 0 to 1000 Pa at the beginning of the syllable and smoothly lowered back to 0 Pa at the end. was reproduced from a spoken CV-syllable for a natural intonation. was initialized with a value appropriate for the consonant and started to approach the value for the vowel at about . For the vowel, the target was set to a value for modal phonation (). However, the initial value was adjusted depending on the consonant. For /b/, /d/ and /g/, this parameter was used to produce different voice-onset times. According to our measurements, the mean VOT was 18 ms for /b/, 22 ms for /d/, and 38 ms for /g/. The rather short VOTs for /b/ and /d/ were roughly adjusted with a value appropriate for slightly-pressed () or modal () phonation. The longer VOTs for /g/ were generated with more abducted vocal folds during the consonant, which are typical for a slightly breathy type of phonation (). This is illustrated by the higher initial value for the glottal abduction in Figure 10. For /l/, /m/ and /n/, the degree of abduction was adjusted as for the vowel. The consonant /r/ was synthesized as voiced uvular fricative with slightly more abducted vocal folds to generate appropriate frication noise at the supraglottal constriction. After synthesis, the amplitude of the stimuli was normalized. All stimuli are contained in the supplemental materials Audio S1, Audio S2, Audio S3, Audio S4, Audio S5, Audio S6, and Audio S7.
Subjects and method
The 56 stimuli were individually presented to 20 German listeners to identify the consonant and the vowel in each syllable. The participants, 9 men and 11 women, were native speakers of German (except one native English speaker, who has lived in Germany for more than 30 years) and 21–57 years old. Sixteen of them had a background in speech therapy or phonetics, and none of them reported any kind of hearing impairment. Each participant first listened to the eight isolated synthetic vowels to get used to the synthetic voice. Then, the stimuli were presented, in a different random order for each participant, in a quiet room using closed earphones. Each stimulus could be repeated once on request. The participants were asked to check one of the consonants “b”, “d”, “g”, “l”, “r”, “m”, “n”, and one of the long German vowels “a”, “e”, “i”, “o”, “u”, “ä”, “ö”, “ü” on a list after listening to each stimulus. They were asked to make their decisions spontaneously (but without an actual time limit) and to check the most similar phoneme in the case of uncertainty.
Results and discussion
Recognition rates of the vowels and consonants in the synthetic syllables are shown in Table 4. With regard to the vowels, the recognition rates varied between 84.3% and 100.0%. However, statistically significant differences were only found between /i:/ and each of /a:/, /e:/, /o:/ and /E:/ by pairwise comparisons using Bonferroni corrected t-tests (). The overall recognition rate of 95.4% is close to that of natural vowels, e.g., in the study by Hillenbrand and Nearey . They reported an average recognition rate of 96% for 12 English vowels in /hVd/ syllables. In the present study, /i:/ and /u:/ had the lowest recognition rate. The confusion matrix in Table 5 shows that 15.7% of the /i:/ vowels were falsely identified as /e:/, and 12.1% of /u:/ as /o:/. This indicates that the articulations of the corner vowels /i:/ and /u:/ were not extreme enough to differ sufficiently from their “neighbors” /e:/ and /o:/, respectively. As described before, the vowels were acoustically optimized with respect to the mean formant frequencies of 30 realizations in different contexts. However, the mean values do not apparently represent the asymptotic underlying targets for all vowels. In future work, considering the formant transitions towards the individual vowel samples could help to obtain more representative vowel targets.
With regard to the consonants, the recognition rates varied between 67.5% and 100.0%. Pairwise comparisons of the recognition rates using Bonferroni corrected t-tests indicated that only the recognition rates of /n/ and /l/ (both of which were 100%) were significantly higher than those for all other consonants (/b/, /d/, /g/, /r/, and /m/) with . The overall recognition rate was 82.4%. When people listen to pseudowords produced by humans, they achieve a higher consonantal recognition rate, e.g., 99% for CV syllables as reported by Klatt , or 98% for the same seven consonants as in the present study in VCV syllables as reported by Broersma and Scharenborg . This should be the goal or upper bound for all programs attempting to synthesize speech. To our knowledge, the present study is the first where the recognition of consonants generated with articulatory speech synthesis was systematically evaluated for a range of different context vowels. Hence, there is no directly comparable baseline with synthetic speech. Somewhat comparable to articulatory speech synthesis is formant synthesis, which is also a parametric speech synthesis technique. Formant synthesis was the dominating method for speech synthesis for several decades, until it was mostly displaced by concatenation systems in the 1990 s. During this time, the rules for generating intelligible phonemes were continuously improved and evaluated, and the consonantal recognition rate in nonsense CV syllables raised from about 75% in the first systems to about 95% in the best systems . However, to achieve the 95% recognition rate, it was necessary to implement very detailed rules to describe the fine spectral details of the different consonants. In contrast, with the articulatory synthesis method in the present study, only a few simple control rules were necessary to achieve a consonantal recognition rate of already 82.4%.
Table 6 shows the confusion matrix of the consonants for a more detailed analysis. /l/ and /n/ were recognized 100% correct, indicating that the proposed coarticulation model can generally simulate the essential articulatory-acoustic variability of consonants. At the opposite end of the scale was /m/ with the lowest recognition rate. All of the falsely identified /m/ were recognized as /n/. This indicates that the feature “nasality” was well simulated by the model, but /m/ was not distinct enough from /n/. Apparently, the articulation of /m/ differs somewhat more from that of /b/ with a lowered velum as assumed in this study, Therefore, using actual articulatory measurements of the nasals to create their reference vocal tract shapes could improve their perceptual discrimination.
Of the plosives, /g/ had approximately a 10% (absolute) higher recognition rate than /b/ and /d/. However, the acoustic errors of the plosives in terms of formant onset frequencies were roughly equal according to Table 3. This indicates the importance of perceptual cues other than the formant transitions for the discrimination of the plosives. One is the VOT, which we roughly reproduced according to our measurements (Table 1). The better recognition of /g/ could in fact be brought forward by its clearly higher VOT compared to /b/ and /d/, because VOT was previously demonstrated to be highly effective in classifying place of articulation for plosives . Another important perceptual cue of voiced plosives is the spectrum of the release burst . In our simulations, the burst was automatically generated by our preliminary noise source model . However, the simulation of noise sources in the time-varying vocal tract is a subject of extensive research in its own right and has not yet created a model that can be considered realistic and complete under all conditions. Therefore, a more realistic noise source model could substantially contribute to a better discrimination of the plosives.
The recognition rate of /r/, which was synthesized as a uvular voiced fricative, was 81.3%. The major confusions occurred in the context of the front vowels /i:/, /e:/ and /E:/. The reason could be that the context-sensitive vocal tract shape for /r/ in the context of /i/ had to be estimated, because it was not recorded with the real-time MRI corpus. Some participants reported after the experiment that they heard a /z/ instead of /r/ in the context of the front vowels, which indicates that the tongue position was too far anterior for the estimated reference shape and (or) that the noise source model generated noise sources that were too far downstream in the vocal tract or had inappropriate spectral properties.
Currently, text-to-speech synthesis is completely dominated by concatenative and statistical parametric speech synthesis techniques. However, the increasing demands for highly expressive and flexible speech synthesis are recognized to be difficult to satisfy with these techniques. Therefore, articulatory speech synthesis is now becoming a serious alternative for speech synthesis again. In particular the increased availability of MR imaging makes it now possible to develop detailed quantitative models of the vocal tract and articulation. In this study we combined static MRI data, dynamic MRI data, and acoustic recordings of the same speaker to build a model of the vocal tract and coarticulation for the synthesis of consonant-vowel syllables. The key component of the model is the modeling of context-sensitive consonant targets based on a linear combination of measured consonant targets in the context of the corner vowels /a/, /i/, and /u/. The advantage of this phenomenological approach is that relatively little data is needed to model the coarticulatory variability of a consonant, and the data can be measured directly. Therefore, the application of the model to other phonemes, speakers, and languages is straightforward, for example using an analog available MRI corpus of a British English speaker . In contrast to the consonants, vowels were modeled as invariant asymptotic targets according to the undershoot model .
To assess the performance of the model, it was used for the synthesis and perceptual evaluation of CV syllables for all combinations of seven consonants and eight vowels. For the vowels, the overall recognition rate of 95.4% was close to that of natural vowels. For the consonants, the overall recognition rate of 82.4% was still below the recognition rate of naturally spoken consonants (about 98% for the same set of consonants ). However, the detailed analysis of the recognition results clearly suggested the possible causes for confusions and the necessary steps to improve the synthesis of the consonants in future work. Currently, the most important limitation of the synthesizer is the noise source model, which generates the turbulence noise for fricatives and the bursts for plosives. The challenge for a noise source model for articulatory speech synthesis is to accurately predict the position, strength and spectral shape of noise sources based on the geometry and the aerodynamic conditions in the vocal tract. However, there is currently no noise source model that can be considered realistic and complete under all possible conditions. In this study, the plosives /b/, /d/, /g/, and the approximant /r/ involved noise sources for bursts and frication, respectively, all of which are known to be relevant perceptual cues for these phonemes , . Therefore, future advances in the development of noise source models will likely increase the recognition rate for these consonants. Despite the current limitations, the achieved recognition performance seems to be already high enough for many text-to-speech applications where higher-level context could be expected to contribute to processes of word recognition and sentence comprehension. To demonstrate this, the supplemental materials Video S1 and Video S2 contain an example of a short German sentence synthesized with the proposed coarticulation model: “Lea und Doreen mögen Bananen. ” (“Lea and Doreen like bananas. ”, [le:aUndo:re:nm2:gNbana:n@n]). The fundamental frequency contour and the phone durations were reproduced from the same sentence spoken by the author of this study. The supplemental Image S1 shows the natural and synthesized sentences side-by-side in terms of oscillograms and spectrograms and illustrates the good agreement between the natural and synthetic formant transitions.
In this study, we only modeled CV syllables, but neither CVC syllables nor syllables with consonant clusters, which are necessary for unlimited speech synthesis. For these cases, the time structure model of the syllable  and models of CC coarticulation (e.g. ) offer initial guidance and will be the subject of future studies. Furthermore, it would be interesting to examine the intelligibility of the synthetic consonants when the degree of coarticulation is adapted in different steps between “full coarticulation” (as in this study) and context-independent articulation of consonants. The context-independent reference shape of a consonant could be obtained by averaging the three reference shapes in the context of the corner vowels to minimize contextual articulatory and acoustic variations. Much of the data analysis and model construction in this study was hand crafted, e.g., the tracing of the contours in the MR images, the manual adjustment of the vocal tract parameters for matching the model and MRI contours, and the determination of the formants in the acoustic recordings. In future work, at least some of these steps could be partly automated to save time when the model is adapted to new speakers and to increase reproducibility. A good candidate for automatization is contour tracing in the MR images, for which good results were achieved, e.g., by Bresch and Narayanan . Finally, the noise source model should be improved to raise the overall recognition performance of the consonants.
Synthetic stimuli with the consonant /b/ created for the perception experiment.
Synthetic stimuli with the consonant /d/ created for the perception experiment.
Synthetic stimuli with the consonant /g/ created for the perception experiment.
Synthetic stimuli with the consonant /l/ created for the perception experiment.
Synthetic stimuli with the consonant /r/ created for the perception experiment.
Synthetic stimuli with the consonant /m/ created for the perception experiment.
Synthetic stimuli with the consonant /n/ created for the perception experiment.
The synthesized German sentence “Leo und Doreen mögen Bananen” with the vocal tract model rendered in 3D. This sentence was reproduced from a naturally spoken sentence in terms of the fundamental frequency and the phone durations.
The same sentence as in Video S1 with the vocal tract model shown in the midsagittal plane.
A comparison between the synthesized (top) and natural (bottom) spectrograms and oscillograms for the sentence in Video S1.
I would like to thank Phil Hoole, Bernd Kröger, and Robert Sader for providing the MRI data used in this study, and Yi Xu, Phil Hoole, Scott Moisik, and Christiane Neuschaefer-Rube for proof-reading the manuscript. I am also grateful to our reference speaker Jörg Dreyer and the participants of the perception experiments. I appreciate the cooperation with Jens Frahm and his group for making initial real-time MRI measurements of VCV syllables with two sagittal slices at a frame rate of 25 Hz that were used to adjust the tongue sides for the context-sensitive consonant shapes.
Conceived and designed the experiments: PB. Performed the experiments: PB. Analyzed the data: PB. Contributed reagents/materials/analysis tools: PB. Wrote the paper: PB.
- 1. Hunt AJ, Black AW (1996) Unit selection in a concatenative speech synthesis system using a large speech database. In: IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-96). Atlanta, Georgia. 373–376.
- 2. Zen H, Tokuda K, Black AW (2009) Statistical parametric speech synthesis. Speech Communication 51: 1039–1064. doi: 10.1016/j.specom.2009.04.004
- 3. Shadle CH, Damper RI (2001) Prospects for articulatory synthesis: A position paper. In: Fourth ISCA ITRW on Speech Synthesis (SSW-4). Perthshire, Scotland. 121–126.
- 4. Mermelstein P (1973) Articulatory model for the study of speech production. Journal of the Acoustical Society of America 53: 1070–1082. doi: 10.1121/1.1913427
- 5. Maeda S (1990) Compensatory articulation during speech: Evidence from the analysis and synthesis of vocal tract shapes using an articulatory model. In: Hardcastle WJ, Marchal A, editors, Speech production and speech modelling, Kluwer Academic Publishers, Boston. 131–149.
- 6. Payan Y, Perrier P (1997) Synthesis of V-V sequences with a 2d biomechanical tongue model controlled by the equilibrium point hypothesis. Speech Communication 22: 185–205. doi: 10.1016/s0167-6393(97)00019-8
- 7. Stark J, Ericsdotter C, Branderud P, Sundberg J, Lundberg HJ, et al.. (1999) The APEX model as a tool in the specification of speaker-specific articulatory behavior. In: 14th International Congress of Phonetic Sciences (ICPhS '99). San Francisco, USA. 2279–2282.
- 8. Badin P, Bailly G, Revéret L, Baciu M, Segebarth C, et al. (2002) Three-dimensional linear articulatory modeling of tongue, lips and face, based on MRI and video images. Journal of Phonetics 30: 533–553. doi: 10.1006/jpho.2002.0166
- 9. Engwall O (2003) Combining MRI, EMA and EPG measurements in a three-dimensional tongue model. Speech Communication 41: 303–329. doi: 10.1016/s0167-6393(02)00132-2
- 10. Dang J, Honda K (2004) Construction and control of a physiological articulatory model. Journal of the Acoustical Society of America 115: 853–870. doi: 10.1121/1.1639325
- 11. van den Doel K, Vogt F, English RE, Fels S (2006) Towards articulatory speech synthesis with a dynamic 3d finite element tongue model. In: 7th International Seminar on Speech Production (ISSP '06). Ubatuba, Brazil.
- 12. Birkholz P, Jackèl D, Kröger BJ (2006) Construction and control of a three-dimensional vocal tract model. In: International Conference on Acoustics, Speech, and Signal Processing (ICASSP'06). Toulouse, France. 873–876.
- 13. Birkholz P (2011) A survey of self-oscillating lumped-element models of the vocal folds. In: Kröger BJ, Birkholz P, editors, Studientexte zur Sprachkommunikation: Elektronische Sprachsignalverarbeitung 2011, TUDPress, Dresden. 47–58.
- 14. Birkholz P, Jackèl D (2004) Inuence of temporal discretization schemes on formant frequencies and bandwidths in time domain simulations of the vocal tract system. In: Interspeech 2004. Jeju Island, Korea. 1125–1128.
- 15. Birkholz P, Jackèl D, Kröger BJ (2007) Simulation of losses due to turbulence in the time-varying vocal system. IEEE Transactions on Audio, Speech and Language Processing 15: 1218–1226. doi: 10.1109/tasl.2006.889731
- 16. Öhman SEG (1967) Numerical model of coarticulation. Journal of the Acoustical Society of America 41: 310–320. doi: 10.1121/1.1910340
- 17. Birkholz P, Kröger BJ (2006) Vocal tract model adaptation using magnetic resonance imaging. In: 7th International Seminar on Speech Production (ISSP'06). Ubatuba, Brazil. 493–500.
- 18. Fowler CA, Saltzman E (1993) Coordination and coarticulation in speech production. Language and Speech 36: 171–195.
- 19. Kühnert B, Nolan F (1999) The origin of coarticulation. In: Hardcastle WJ, Hewlett N, editors, Coarticulation: Theory, Data and Techniques, Cambridge University Press. 1–30.
- 20. Båvegård M (1995) Introducing a parametric consonantal model to the articulatory speech synthesizer. In: 4th European Conference on Speech Communication and Technology (EUROSPEECH '95). Madrid, Spain. 1857–1860.
- 21. Carré R, Chennoukh S (1995) Vowel-consonant-vowel modeling by superposition of consonant closure on vowel-to-vowel gestures. Journal of Phonetics 23: 231–241. doi: 10.1016/s0095-4470(95)80045-x
- 22. Story BH (2009) Vowel and consonant contributions to vocal tract shape. Journal of the Acoustical Society of America 126: 825–836. doi: 10.1121/1.3158816
- 23. Lindblom B, Sussman HM (2012) Dissecting coarticulation: How locus equations happen. Journal of Phonetics 40: 1–19. doi: 10.1016/j.wocn.2011.09.005
- 24. Sussman HM, McCaffrey HA, Matthews SA (1991) An investigation of locus equations as a source of relational invariance for stop place categorization. Journal of the Acoustical Society of America 90: 1309–1325. doi: 10.1121/1.401923
- 25. Browman CP, Goldstein L (1992) Articulatory phonology: An overview. Phonetica 49: 155–180. doi: 10.1159/000261913
- 26. Kröger BJ (1993) A gestural production model and its application to reduction in German. Phonetica 50: 213–233. doi: 10.1159/000261943
- 27. Howard IS, Messum P (2011) Modeling the development of pronunciation in infant speech acquisition. Motor Control 15: 85–117.
- 28. Kröger BJ, Kannampuzha J, Neuschaefer-Rube C (2009) Towards a neurocomputational model of speech production and perception. Speech Communication 51: 793–809. doi: 10.1016/j.specom.2008.08.002
- 29. Guenther FH (1995) Speech sound acquisition, coarticulation, and rate effects in a neural network model of speech production. Psychological Review 102: 594–621. doi: 10.1037/0033-295x.102.3.594
- 30. Wickelgren WA (1969) Context-sensitive coding, associative memory, and serial order in (speech) behaviour. Psychology Review 76: 1–15. doi: 10.1037/h0026823
- 31. Wells J, Barry W, Grice M, Fourcin A, Gibbon D (1992) Standard computer compatible transcription. Esprit Project 2589 (SAM). Doc. no SAM-UCL-037, Phonetics and Linguistics Dept. UCL, London.
- 32. Kröger BJ, Winkler R, Mooshammer C, Pompino-Marschall B (2000) Estimation of vocal tract area function from magnetic resonance imaging: Preliminary results. In: 5th Seminar on Speech Production: Models and Data. Kloster Seeon, Bavaria. 333–336.
- 33. Kröger BJ, Hoole P, Sader R, Geng C, Pompino-Marschall B, et al. (2004) MRT-Sequenzen als Datenbasis eines visuellen Artikulationsmodells. HNO 52: 837–843. doi: 10.1007/s00106-004-1097-x
- 34. Niebergall A, Zhang S, Kunay E, Keydana G, Job M, et al.. (2012) Real-time MRI of speaking at a resolution of 33 ms: Undersampled radial FLASH with nonlinear inverse reconstruction. Magnetic Resonance in Medicine doi:10.1002/mrm.24276.
- 35. Beier T, Neely S (1992) Feature-based image metamorphosis. Computer Graphics (SIGGRAPH '92 Proceedings) 26: 35–42. doi: 10.1145/142920.134003
- 36. Birkholz P (2005) 3D-Artikulatorische Sprachsynthese. Logos Verlag Berlin.
- 37. Edwards J, Harris KS (1990) Rotation and translation of the jaw during speech. Journal of Speech and Hearing Research 33: 550–562.
- 38. Engwall O (1999) Vocal tract modeling in 3D. TMH-QPSR 1: 1–8.
- 39. Serrurier A, Badin P (2008) A three-dimensional articulatory model of the velum and nasopharyngeal wall based on MRI and CT data. Journal of the Acoustical Society of America 123: 2335–2355. doi: 10.1121/1.2875111
- 40. Abry C, Boë LJ (1986) \Laws" for lips. Speech Communication 5: 97–104.
- 41. Macaulay R (1994) The social art: Language and its uses. New York: Oxford University Press.
- 42. Lindblom B (1963) Spectrographic study of vowel reduction. Journal of the Acoustical Society of America 35: 1773–1781. doi: 10.1121/1.1918816
- 43. Stevens KN (1989) On the quantal nature of speech. Journal of Phonetics 17: 3–46.
- 44. Sondhi MM (1983) An improved vocal tract model. In: Proceedings of the 11th ICA. Paris, France. 167–170.
- 45. Stevens KN (1998) Acoustic Phonetics. The MIT Press, Cambridge, Massachusetts.
- 46. Flanagan JL (1965) Speech Analysis Synthesis and Perception. Springer-Verlag, Berlin.
- 47. Birkholz P, Kröger BJ, Neuschaefer-Rube C (2010) Articulatory synthesis and perception of plosivevowel syllables with virtual consonant targets. In: Interspeech 2010. Makuhari, Japan. 1017–1020.
- 48. Narayanan SS, Alwan AA, Haker K (1997) Toward articulatory-acoustic models for liquid approximants based on MRI and EPG data. Part I. The laterals. Journal of the Acoustical Society of America 101: 1064–1077. doi: 10.1121/1.418030
- 49. Narayanan SS, Alwan AA, Haker K (1995) An articulatory study of fricative consonants using magnetic resonance imaging. Journal of the Acoustical Society of America 98: 1325–1347. doi: 10.1121/1.413469
- 50. Kewley-Port D (1982) Measurement of formant transitions in naturally produced stop consonantvowel syllables. Journal of the Acoustical Society of America 72: 379–389. doi: 10.1121/1.388081
- 51. Dang J, Honda K, Suzuki H (1994) Morphological and acoustical analysis of the nasal and paranasal cavities. Journal of the Acoustical Society of America 96: 2088–2100. doi: 10.1121/1.410150
- 52. Titze IR (1989) A four-parameter model of the glottis and vocal fold contact area. Speech Communication 8: 191–201. doi: 10.1016/0167-6393(89)90001-0
- 53. Birkholz P, Kröger BJ, Neuschaefer-Rube C (2011) Model-based reproduction of articulatory trajectories for consonant-vowel sequences. IEEE Transactions on Audio, Speech and Language Processing 19: 1422–1433. doi: 10.1109/tasl.2010.2091632
- 54. Hillenbrand JM, Nearey TM (1999) Identification of resynthesized /hVd/ utterances: Effects of formant contour. Journal of the Acoustical Society of America 105: 3509–3523. doi: 10.1121/1.424676
- 55. Klatt DH (1987) Review of text-to-speech conversion for English. Journal of the Acoustical Society of America 82: 737–793. doi: 10.1121/1.395275
- 56. Broersma M, Scharenborg O (2010) Native and non-native listeners' perception of English consonants in different types of noise. Speech Communication 52: 980–995. doi: 10.1016/j.specom.2010.08.010
- 57. Alwan A, Jiang J, Chen W (2011) Perception of place of articulation for plosives and fricatives in noise. Speech Communication 53: 195–209. doi: 10.1016/j.specom.2010.09.001
- 58. Steiner I, Richmond K, Marshall I, Gray CD (2012) The magnetic resonance imaging subset of the mngu0 articulatory corpus. Journal of the Acoustical Society of America 131: EL106–EL111. doi: 10.1121/1.3675459
- 59. Xu Y, Liu F (2006) Tonal alignment, syllable structure and coarticulation: Toward an integrated model. Italian Journal of Linguistics 18: 125–159.
- 60. Marin S, Pouplier M (2010) Temporal organization of complex onsets and codas in American English: Testing the predictions of a gestural coupling model. Motor Control 14: 380–407.
- 61. Bresch E, Narayanan S (2009) Region segmentation in the frequency domain applied to upper airway real-time magnetic resonance images. IEEE Transactions on Medical Imaging 28: 323–338. doi: 10.1109/tmi.2008.928920