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A longitudinal study of infants’ early speech production and later letter identification

  • Kelly Farquharson ,

    Contributed equally to this work with: Kelly Farquharson, Tiffany P. Hogan, Lesa Hoffman, Jordan R. Green

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing

    Affiliation School of Communication Science and Disorders, Florida State University, Tallahassee, Florida, United States of America

  • Tiffany P. Hogan ,

    Contributed equally to this work with: Kelly Farquharson, Tiffany P. Hogan, Lesa Hoffman, Jordan R. Green

    Roles Conceptualization, Resources, Writing – original draft, Writing – review & editing

    Affiliation Department of Communication Sciences and Disorders, MGH – Institute of Health Professions, Boston, Massachusetts, United States of America

  • Lesa Hoffman ,

    Contributed equally to this work with: Kelly Farquharson, Tiffany P. Hogan, Lesa Hoffman, Jordan R. Green

    Roles Formal analysis

    Affiliation Child Language Doctoral Program, University of Kansas, Lawrence, Kansas, United States of America

  • Jun Wang ,

    Roles Data curation, Methodology, Software, Writing – review & editing

    ‡ These authors also contributed equally to this work.

    Affiliations Department of Biomedical Engineering, University of Texas-Dallas, Richardson, Texas, United States of America, Callier Center for Communication Disorders, University of Texas-Dallas, Dallas, Texas, United States of America

  • Kimber F. Green ,

    Roles Data curation, Project administration

    ‡ These authors also contributed equally to this work.

    Affiliation Kimber Green Therapies, Boston, Massachusetts, United States of America

  • Jordan R. Green

    Contributed equally to this work with: Kelly Farquharson, Tiffany P. Hogan, Lesa Hoffman, Jordan R. Green

    Roles Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Resources, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Department of Communication Sciences and Disorders, MGH – Institute of Health Professions, Boston, Massachusetts, United States of America


Letter identification is an early metric of reading ability that can be reliability tested before a child can decode words. We test the hypothesis that early speech production will be associated with children’s later letter identification. We examined longitudinal growth in early speech production in 9 typically developing children across eight occasions, every 3 months from 9 months to 30 months. At each occasion, participants and their caregivers engaged in a speech sample in a research lab. This speech sample was transcribed for a variety of vocalizations, which were then transformed to calculate consonant-vowel ratio. Consonant-vowel ratio is a measure of phonetic complexity in speech production. At the age of 72 months, children’s letter knowledge was measured. A multilevel model including fixed quadratic age change and a random intercept was estimated using letter identification as a predictor of the growth in early speech production from 9–30 months, measured by the outcome of consonant-vowel ratio. Results revealed that the relation between early speech production and letter identification differed over time. For each additional letter that a child identified, their consonant-vowel ratio at the age of 9 months increased. As such, these results confirmed our hypothesis: more robust early speech production is associated with more accurate letter identification.


Early language skills such as phonological awareness [1, 2, 3, 4], vocabulary [5, 6, 7, 8], syntax [9] and letter knowledge [10, 11, 12] are predictive of reading performance. Thus, children with weak early language skills are at risk for reading impairments. Unfortunately, these ‘early’ language skills are not measured reliably until 3–5 years of age [13]. In this study, we hypothesize that an earlier language ability, speech production, measured in infancy and toddlerhood will be associated with later letter knowledge. If confirmed, early speech-based metrics have the potential to identify children at risk for reading impairment well before other language skills can be reliably measured.

Early speech production is perhaps best defined as “utterances of speech as well as other (nonmeaningful) utterances that could be said to be phonotactically well-formed for some natural or potential natural language” [14]. Early speech production is an informative metric that is directly related to later outcomes, such as lexical development [15, 16, 17], phonological awareness [18], letter-sound correspondence [9] and later reading outcomes [19, 20, 21, 22]. In fact, early speech production is related to how young children learn new words [17] as well as the strength of their phonological memory [23]. However, the methods by which early speech production is measured in each of these studies is variable. For instance, some researchers have measured the proportion of vocalizations that include a consonant and have reported that this measure of early speech production is a robust predictor of vocabulary development [17, 23, 24, 25]. Scarborough (1990) reported that children’s early speech production accuracy—measured by percentage of consonants correct—predicted their knowledge of later letter-sound correspondences. Importantly, in her study, Scarborough (1990) found that children with many consonant errors in their early speech were more likely to be later diagnosed with a reading impairment. Similar reports have shown that speech sound errors related to a developmental sequence of acquisition (e.g., early, middle, or late 8 phonemes; [26, 27]) in preschool is related to performance on phonological awareness tasks [18]. Finally, a robust line of work has examined a variety of speech-related measures and their relations to later reading outcomes [19, 20, 21, 22]. For example, Smith et al. (2008) found that pause time during speech production of 3-year-olds was related to their 3rd grade reading comprehension and nonword decoding. In a related study, Lambrecht-Smith (2009) reported that children diagnosed with a reading impairment in 2nd grade or later had fewer polysyllabic words in their early speech production. Additionally, these children had a lower number of different words, which is a measure of lexical diversity.

Lexical diversity, or vocabulary complexity, has significant relations to both early speech production and later literacy skills. Children who produce more complex phonetic forms in their babble and early speech production are predicted to have a larger expressive vocabulary [16]. Specifically, the proportion of vocalizations that include a consonant is a robust predictor of vocabulary development. Additionally, Stoel-Gammon (1989) showed that depressed vocabularies were evident in children who exhibited limited phonetic repertoires during early speech production. Finally, Oller, Eilers, Neal, and Schwartz (1999) reported that a delayed onset of canonical babbling, or the use of repeated consonant and vowel sequences, was related to small vocabularies in children at 18-, 24-, and 30-months-old. As such, there is evidence to support the relation between the development of the phonological system, measured by early speech production, and later language development, measured by vocabulary output. Indeed, vocabulary is a robust predictor of literacy skills [5, 6, 7, 8]; Murphy et al. (2016) reported that children with lower levels of lexical quality in preschool had weaker reading comprehension, listening comprehension, and word reading skills in their first grade.

These examples of less sophisticated speech production paired with later reading impairments points to a broad phonologically based issue that may begin with early speech production. It is plausible that deficits in both speech production and reading indicate overall weak phonological representations. Phonological representations refer to the storage of word-level phonological information in long-term memory [28, 29, 30]. The quality of the representations will determine the child’s speech production accuracy [30, 31, 32], vocabulary size [15], and word reading abilities [28, 33, 34, 35]. Links between early speech and reading skill may be mediated by emerging phonologic skills—both speech production and word reading rely on well-defined phonological representations. Although accurate word reading skills are not expected until a child receives formal schooling, there is mounting evidence that foundational skills necessary for literacy are built in infancy.

Thus, it follows that the strength of phonological representations is critical for mapping phonemes to orthographic, or letter, representations [4, 36, 37]. For example, Justice, Pence, Bowles, and Wiggins (2006) showed that children are more likely to know letters that corresponded with earlier acquired speech sounds (e.g., B and /b/) as compared to letters that corresponded with later acquired speech sounds (e.g., R and /r/). Similarly, Treiman, Weatherston, and Berch (1994) and McBride-Chang (1999) report that the name of the letter itself influences a child’s ability to learn it. That is, letter names that are comprised of a consonant-vowel sequence (e.g., /bi/ for B) are easier to learn than those comprised of a vowel-consonant sequence (e.g., /ɛf/ for F). These findings underscore the importance of phonological properties that influence letter name knowledge and how children decode and spell words [38, 39]. Two additional examples of this are spelling the word “wife” with the letter “Y” [40] or more easily decoding words that start with the letter B followed by the vowel /i/ because of the influence of the letter’s name (i.e., /bi/ as in “beach”; [41]).

Theoretical support for the relation between phonology and orthography is further outlined in the self-teaching hypothesis [42]. “The self-teaching hypothesis proposes that only the ability to translate a printed letter string into its spoken form offers a reliable means of independently identifying new letter strings” [43]. The hypothesis emphasizes the reciprocal relations between the development of phonological and orthographic representations [44, 45]. For instance, the phoneme /f/ can be represented orthographically with the letter “F” (as in fan), or the letter sequences “PH” (as in phone) and “GH” (as in laugh). On the other hand, the letter “S” can be represented phonologically by the phoneme /s/ (as in sun), the phoneme “sh” (as in sure), or the phoneme /z/ (as in trees).

According to the self-teaching hypothesis, children who have more frequent exposure to their respective phonology, via early speech production, and to letter patterns experience a “self-teaching” mechanism that leads to more successful reading experiences. During this process, children are strengthening mappings between phonological and orthographic representations. Thus, learning to correctly articulate speech sounds may augment the phonological knowledge necessary for learning letter names and sounds. Pursuant to the aims of the present investigation, we explored how the frequency and complexity of early speech production influences letter knowledge. If we confirm that there is a relation between these two constructs, this may provide additional support to the self-teaching hypothesis. Specifically, a relation between the development of early speech production and later letter knowledge may substantiate the reciprocal relation between phonological and orthographic knowledge, as outlined in the self-teaching hypothesis. Given the robust predictive relation between orthographic, or letter, knowledge and later word reading abilities [4,12,40, 41, 46, 47, 48, 49, 50, 51, 52, 53], it is prudent to explore avenues that may lead to earlier identification of literacy strengths and weaknesses.

In this study, we measured consonant growth in early speech production in typically developing infants longitudinally from 9–30 months of age, and then determined how that change was associated with the number of letters known at 72-months of age. Sounds present in a child’s early speech production comprise their phonetic complexity, or the phonological variation in babble [54, 55, 56]. Specifically, we asked, what is the strength of association between the number of consonants produced during early speech production from 9–30 months and the ability to identify letters at 72 months (6-years-old)? We hypothesized that the number of letters identified at 72 months would be associated with growth during early speech production from 9–30 months. Specifically, we predict that children who know more letters at 72-months-old will have produced more consonants early in their speech production trajectory, compared to children who know fewer letters at 72-months old. This study represents a first step in a line of inquiry aimed at determining early and sensitive measures for early identification of reading risk.

Materials and methods


This research was approved by the Institutional Review Board (IRB) at the University of Nebraska-Lincoln. Written informed consent was obtained from the parents of all participants. Participants were selected from a larger study examining motor development for speech production (see [57]). As a part of the larger study, families with infants were recruited to participate in data collection sessions every three months from 3-months-old to 30-months-old. Participants were recruited for the larger study through flyers posted in pediatrician’s offices and through ads placed in local newspapers. As an extension of the original study, participants were invited to continue data collection sessions every six months after 30 months of age. For the present study, we have included 8 occasions from infants from the ages of 9- to 30-months-old and one additional data collection session at 72-months-old.

The age range of 9- to 30-months was chosen for three primary reasons. First, we chose 9-months as our starting point to align with the time when most babies are in the canonical and/ or variegated babbling stages [58, 59, 60]. Second, this age range captured a common period of substantial growth in both phonological and lexical knowledge [61]. Third, and more practically speaking, our final occasion of 30-months corresponded with the final data collection session of the larger study. All data collection took place in a research lab.

Sample size was determined based on the number of children who continued participation until 72 months of age. The present study, then, reflects data from 9 typically developing infants (8 girls, 1 boy) for a total of 66 data points from ages 9 to 30 months. Of the 9 children, 6 had all 8 occasions, 1 had 7 occasions, 1 had 6 occasions, and 1 had 5 occasions. All infants were from English speaking families in the Midwest and were born at term with no neurological, vision, hearing, or physical impairments. Hearing was screened at every data collection session using an otoacoustic emissions procedure at 2, 3, 4, 5 kHz [62]. Occasionally, one of the infants did not pass the hearing screening because he/she was fussy, vocalizing, or congested, which are situations in which reliable otoacoustic emission readings cannot be obtained; however, no infants failed consecutive tests.


A trained and certified speech-language pathologist conducted all developmental testing, and collected all speech samples in a research lab (testing methods described below). Early speech samples were audio and video recorded and were obtained while the children were placed in a car seat and secured using a five-point harness. The car seat was attached to a dental chair and the child was positioned to face the primary caregiver. The infant’s primary caregiver, who was typically the mother, sat in front of the child.

Two communicative context conditions were examined during each 45-minute data collection session. The first condition was a natural “free-play” condition. Parents were provided with a basket of toys and instructed to play with their child. The second condition, which started approximately 10 minutes after free-play, was more structured than the first. The caregiver was given a set of toys and asked to take a few turns with the toy, and then to pause and wait to see what the child would do (“play” condition). Three different sets of toys were provided for each parent–child dyad. One set of toys was designed to elicit requesting, including toys in transparent containers. The second set of toys was designed to elicit joint attention and included picture books and “surprise” bags (bags with various toys inside). The third set of toys encouraged social interaction. Toys included pretend food, dolls, and pretend tools. Each set of toys was used for approximately five minutes before being replaced with the next.

Data transcription.

The interactions between the parents and the child provided a broad sample of utterances that were transcribed for babble, vocalizations, words, and phrases. Audio and video recordings of the speech samples were transcribed by a trained speech language pathologist. The transcription method was developed based upon the work of Oller and Eilers (1989), but additionally motivated by Vihman and McCune (1994). Based upon this previously established coding scheme, utterances were coded as: word, possible word, single babble, reduplicated babble, or variegated babble [14]. A word production referred to words that were produced in context (e.g., “its Mickey”; “two”; “please”). Words had no more than one or two phonetic variations due to age-expected speech error patterns (e.g., substituting a /t/ for a /k/ phoneme, as in “tan” for “can”). Possible word referred to a production that had an identifiable referent (i.e., object in the room) or was accompanied by a gesture or imitation (e.g., “bir” for “bird”; “ru” for “run”). For these utterances, the transcriber was at least 50% confident that the child was attempting a word and at least one phoneme from the target word was required to be present. Productions were coded as a single babble if produced in a CV, VC, CVC, CCV, CCVC, VCC, CVV, or VCV pattern (e.g., “ooofff”; “dada”; “teebee”). Reduplicated babble contained the same C and V (i.e., gagagaga) and variegated babble contained different C and/ or V (i.e., gagu; gaba). For each data collection session included in the present study, at least 50 vocalizations are included; no utterances were excluded. A second researcher coded 20% of the vocalization samples across all ages. Interrater reliability was 87.5%


To index each child’s level of speech development, we computed the consonant-vowel (CV) ratio (i.e., the number of consonants produced divided by the number of vowels produced) for each transcribed speech sample. CV ratios change as children add consonants and consonant clusters to their phonetic inventories, which are initially predominated by vowels. CV ratios have been demonstrated to increase with age and to adequately measure phonetic complexity [25, 63, 64, 65]. Further, CV ratios allow for a robust examination of speech production throughout development, particularly as the linguistic context changes. That is, as children move from a more primitive reduplicated babble into more advanced word production, this single measure captures the complexity of that linguistic and phonetic growth.

Our time-invariant predictor variable was letter identification. At age 72 months (an age at which most children should be able to identify all letters; [66], children were given the Woodcock Reading Mastery Test-Revised (WRMT-R; [67]) Letter Identification subtest. The letter identification subtest contains 51 upper and lower case alphabet letters in various fonts. Font variety allows for examination of both letter knowledge and general print exposure and adds a level of complexity to a letter naming task. Each child was presented with 3–6 items on multiple easel pages and asked to name the same letters in the same order. If the child responded with the correct name of the letter, the item is scored as correct. The split-half reliability for this subtest was r = 0.94 [67].

Our analytical approach used CV ratio as the outcome variable and letter identification as the predictor variable. In a sense, this approach can be considered “reverse prediction” in that, we are examining the extent to which individual differences in a later developing skill (i.e., letter identification) may be foreshadowed by differences in earlier developing skills. Practically speaking, this approach was necessary to examine the relation between our constructs of interest. We expand on the details of this approach below.


Descriptive statistics for early speech production (as measured by CV ratio) at ages 9 to 30 months and letter identification (letter ID) at age 72 months are presented in Table 1.

Table 1. Descriptive statistics for consonant-vowel ratio and letter identification.

As reported in Table 1, the early speech production outcome of CV ratio was multiplied by 100 to offer more precision in reporting and to create a more interpretable scale (see [68] for a similar approach). To examine its longitudinal change across eight occasions from 9 to 30 months, we estimated multilevel models using residual maximum likelihood (REML) within Statistical Analysis Software (SAS) PROC MIXED. Although equivalent to traditional least squares estimation for complete data, our use of full-information REML estimation allows the inclusion of participants with missing outcomes under an assumption of missing at random (i.e., conditionally random given the participant’s other data). Further, REML provides unbiased random effects variances in small samples, and thus is preferable to maximum likelihood estimation for the present sample [69]. Accordingly, the significance of random effects was evaluated through −2LL differences between nested models (i.e., likelihood ratio tests), whereas the significance of fixed effects was evaluated via univariate and multivariate Wald tests using Kenward–Roger denominator degrees of freedom (which is also preferred for small samples).

To begin, an empty means, random intercept only model was estimated to partition the variance in CV ratio. The intraclass correlation (ICC) for the ratio of random intercept variance to total variance was .17, which indicates that 17% of the variance is from individual mean differences in children’s early speech production complexity over time. We also estimated a saturated means, random intercept model (with a separate mean for each occasion) to examine the shape of the average trajectory (Fig 1). To approximate this trajectory, fixed effects of linear and quadratic age (centered such that 0 = age 12 months) were then included. There remained a significant difference between the model-predicted trajectory and that given by the saturated means, F(5, 51) = 3.47, p < .01, which was largely due to a higher-than-predicted CV ratio at age 9 months. To capture this deflection, we added a piecewise linear slope (coded −3 for 9 months and 0 otherwise) to indicate the difference in change per month from ages 9 to 12 months. After doing so, the model-predicted trajectory did not differ significantly from the saturated means as desired, F(4, 51) = 1.03, p = .40. We then tested for individual differences in change and heterogeneity of variance over age, but none were found. More specifically, adding random linear or quadratic effects of age (and their covariances with the random intercept) did not significantly improve model fit, nor did adding heterogeneity of residual variance across age.

Fig 1. Predicted and observed means for early speech production scores across months- fixed quadratic, random intercept model.

Thus, as shown in Fig 1, the best-fitting unconditional model for time included fixed effects of linear and quadratic age, a fixed slope deflection from 9 to 12 months, and a random intercept variance. As reported in the first set of columns in Table 2, at age 12 months (the intercept), the predicted mean consonant-vowel ratio was 89.63 with an instantaneous linear rate of growth of 5.55 per month that became less positive by twice the quadratic rate of change of −0.22 per month. In other words, the rate of growth in early speech production slowed down over time. The fixed 9-month slope deflection indicated that, relative to after 12 months of age, from ages 9 to 12 months the linear rate of growth was more negative by 11.14 per month. Overall, the effects related to change over age accounted for 42% of the residual variance.

Table 2. Results for longitudinal models predicting consonant-vowel ratio.

We then examined the effects of letter identification at 72 months (centered such that 0 = 35) in predicting early speech production over time—as a moderator of each fixed effect in the trajectory of CV ratio over age. The interaction of letter identification with the 9-month slope deflection was nonsignificant, and was thus removed. The final model is reported in the second set of columns in Table 2, in which the fixed effects for the intercept, linear age, and quadratic age refer to a reference child with a letter identification score of 35. For every additional letter identified, the quadratic rate of change was expected to be significantly less negative by 0.03. This pattern of interaction is depicted in Fig 2—children with better letter identification at age 72 months had less curvature in their earlier pattern of growth. Said differently, the effect of letter identification was largest in the earliest ages and diminished in an accelerated fashion over time. The interactions of letter identification by linear and quadratic age accounted for 7% of the remaining residual variance.

Fig 2. Predicted means of early speech production scores for children with high and low letter identification.


This study tested the hypothesis that early speech production is associated with later letter knowledge. To address this question, we examined the relation between growth in early speech production, indexed by consonant-vowel ratio, from 9 months to 30 months and letter identification skills at 72 months in 9 typically developing children. In support of our hypothesis, we observed that the trajectory of early speech production was related to later letter identification, and that early speech production was different for children with high levels of letter identification than for children with low levels of letter identification.

The association between early speech production and later letter identification was expected based on known links between phonologic and orthographic representations [36, 37]. When the connection between phonologic representations and orthographic representations is strong, children develop the appropriate letter-sound correspondences to become skilled readers [28, 31, 33, 35]. In the current study, children with higher letter identification also had more robust early speech production skills at 9-months-old, suggesting that an early, strong foundation in phonological abilities, via early speech production, is related to early, well-established orthographic representations.

Orthographic learning offers another explanation for the relations between early speech production skills and later letter identification abilities. The relation between phonology and orthography is reciprocal; just as children need to learn how phonology maps onto orthography, they also must to learn the different ways that orthography maps onto phonology. During early reading development, children are able to acquire rudimentary mappings from a word's letter sequence to its pronunciation and vice versa. Our data support a strong association between early speech production and later letter identification skills. Specifically, we found that early speech production—measured as consonant-vowel ratio—was related to letter knowledge such that greater early speech production at 9-months-old was related to knowing more letters later. This aligns with previous research that indicates that the onset of consonant production is related to later language outcomes, such as referential vocabulary [16]. This is particularly important as McGillon et al. (2017) indicated that this early measure of speech production indicates phonological readiness. Thus, it is plausible that children who use more consonants and consonant clusters during early speech production have had more exposure to and experience with those phonemes. These early speech production experiences lend themselves to a stronger foundation for learning orthographic information. As such, it is likely that early speech production skills may also serve as a sensitive early indicator for possible deficits in acquiring letter-sound correspondence skills (see also [9]).

In the current study, children with lower levels of letter identification ability exhibited less robust early speech production than did children with higher levels of letter identification. However, when examining early speech production growth patterns, children who knew fewer letters actually grew more over time. Although this may seem counterintuitive, it is reflective of the gap that exists between children with high and low letter knowledge. That is, children with higher letter knowledge started out with stronger CV ratios at 9-months-old when compared to children who knew fewer letters. As a result, children who knew fewer letters needed growth in early speech production in order to “catch up” to children who knew more letters. This result also highlighted the group differences at the beginning and ending of the observation period, at 9 and at 30 months. These end effects raise the possibility that the achievement gap between these groups may widen [70]. The apparent overlap in speech production skills between 15 and 24 months may be due to the wide variability in speech performance that is characteristic of children within this age range [55]. It is, therefore, plausible that differences are more visible on the “tails” of that unstable developmental time period. In addition, this gap is often seen with respect to early vocabulary [71, 72], phonological awareness, [3, 73] and reading development [72] and is often referred to as the Matthew Effect. In the Matthew Effect, “the rich get richer and the poor get poorer”. Although the children in this study were typically developing, we do see a gap in their letter knowledge that is also present in their early speech production as young as 9-months-old. This gap has clinical implications for current practices used for determining eligibility for early intervening services.


Although this study is an important step in determining earlier behavioral predictors of later reading outcomes, it has a few limitations. First, all children in the sample were typically developing, and therefore the range of variability in letter identification skills was restricted. Future studies will benefit from samples of children with less letter knowledge who have a higher risk of developing a reading impairment. Next, we used a single measure to refer to early speech production. Future work should consider the addition of multiple measures of speech production to create robust latent variables. In addition, more empirical data that support methods to predict later literacy skills from early speech production skills will have robust implications for clinical and theoretical frameworks. Finally, this study included a small sample of children. Although we were able to estimate robust statistical models due to having multiple time points per participant, we hope to conduct future research including larger samples of children. Future work should also consider examining both letter-name and letter-sound knowledge over time to determine the differential influences of early speech production on these two skills.


The results of our study support a connection between growth in early speech production abilities and later letter identification skills, which is a strong predictor of later word reading skills [10, 11]. Our study provides the necessary first step to establishing the validity of measuring early speech production to predict later reading achievement. Although additional work is needed to explore these relations in children with varying skill levels, there are robust clinical implications from this work. First, children who are late to develop consonants in their early babble may be at risk for a slower acquisition of later language skills, including vocabulary and letter knowledge. Second, speech-language pathologists (SLPs) are on the front lines of early identification of children who may be at risk for reading difficulties. Frequently, SLPs are the first professionals to work with a child who has had delayed development of speech and language. As such, the role of the SLP in the prevention, identification, and treatment of children at risk is paramount. In particular, our data point towards a potential need to measure speech and language skills at multiple time points for children in the birth-to-three range, and to consider measures that include spontaneous speech samples to augment the results of static standardized assessments. Finally, parents and other professionals who work with young children have an opportunity to build upon the speech and language skills of infants and toddlers by modeling strong speech production, expanding children’s utterances using grammatically correct language, and engaging in print-rich literacy activities, such as shared book reading.


This work has been supported by the National Institute of Health, National Institute on Deafness and Other Communication Disorders (R01 DC006463), awarded to Jordan R. Green. The funder provided support in the form of salaries for authors [KFG, JRG], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. Commercial affiliation of KFG did not play a role in the study. The authors would like to thank Cynthia Didion, Antje Mefferd, and Cara Ullman for their assistance with data collection and analysis.


  1. 1. Catts HW. The relationship between speech-language impairments and reading disabilities. Journal of Speech, Language, and Hearing Research. 1993 Oct 1;36(5):948–58.
  2. 2. Gillon GT. The efficacy of phonological awareness intervention for children with spoken language impairment. Language, Speech, and Hearing Services in Schools. 2000 Apr 1;31(2):126–41. pmid:27764385
  3. 3. Hogan TP, Catts HW, Little TD. The relationship between phonological awareness and reading: Implications for the assessment of phonological awareness. Language, speech, and hearing services in schools. 2005 Oct 1;36(4):285–93. pmid:16389701
  4. 4. Wagner RK, Torgesen JK, Rashotte CA. Development of reading-related phonological processing abilities: New evidence of bidirectional causality from a latent variable longitudinal study. Developmental psychology. 1994 Jan;30(1):73.
  5. 5. Gathercole SE, Baddeley AD. Evaluation of the role of phonological STM in the development of vocabulary in children: A longitudinal study. Journal of memory and language. 1989 Apr 1;28(2):200–13.
  6. 6. Metsala JL. Young children's phonological awareness and nonword repetition as a function of vocabulary development. Journal of Educational Psychology. 1999 Mar;91(1):3.
  7. 7. Murphy KA, LARRC, Farquharson K. Investigating profiles of lexical quality in preschool and their contribution to first grade reading. Reading and Writing. 2016 Nov 1;29(9):1745–70.
  8. 8. Muter V, Hulme C, Snowling MJ, Stevenson J. Phonemes, rimes, vocabulary, and grammatical skills as foundations of early reading development: evidence from a longitudinal study. Developmental psychology. 2004 Sep;40(5):665. pmid:15355157
  9. 9. Scarborough HS. Very early language deficits in dyslexic children. Child development. 1990 Dec 1;61(6):1728–43. pmid:2083495
  10. 10. Catts HW, Fey ME, Tomblin JB, Zhang X. A longitudinal investigation of reading outcomes in children with language impairments. Journal of speech, Language, and hearing Research. 2002 Dec 1;45(6):1142–57. pmid:12546484
  11. 11. Gallagher A, Frith U, Snowling MJ. Precursors of literacy delay among children at genetic risk of dyslexia. The Journal of Child Psychology and Psychiatry and Allied Disciplines. 2000 Feb;41(2):203–13.
  12. 12. Torppa M, Poikkeus AM, Laakso ML, Eklund K, Lyytinen H. Predicting delayed letter knowledge development and its relation to grade 1 reading achievement among children with and without familial risk for dyslexia. Developmental psychology. 2006 Nov;42(6):1128. pmid:17087547
  13. 13. Duff FJ, Reen G, Plunkett K, Nation K. Do infant vocabulary skills predict school-age language and literacy outcomes?. Journal of Child Psychology and Psychiatry. 2015 Aug 1;56(8):848–56. pmid:25557322
  14. 14. Oller DK, Eilers RE. A natural logic of speech and speech-like acts with developmental implications. First language. 1989 Jan;9(6):225–43.
  15. 15. Majorano M, Vihman MM, DePaolis RA. The relationship between infants’ production experience and their processing of speech. Language Learning and Development. 2014 Apr 3;10(2):179–204.
  16. 16. McCune L, Vihman MM. Early phonetic and lexical development: A productivity approach. Journal of Speech, Language, and Hearing Research. 2001 Jun 1;44(3):670–84. pmid:11407570
  17. 17. Vihman MM, DePaolis RA, Keren-Portnoy T. The role of production in infant word learning. Language Learning. 2014 Sep 1;64(s2):121–40.
  18. 18. Mann V. A., & Foy J. G. (2007). Speech development patterns and phonological awareness in preschool children. Annals of Dyslexia, 57(1), 51–74. pmid:17849216
  19. 19. Lambrecht-Smith S. Early phonological and lexical markers of reading disabilities. Reading and Writing. 2009 Jan 1;22(1):25–40.
  20. 20. Lambrecht-Smith S, Roberts JA, Locke JL, Tozer R. An exploratory study of the development of early syllable structure in reading-impaired children. Journal of learning disabilities. 2010 Jul;43(4):294–307. pmid:20581371
  21. 21. Smith AB, Roberts J, Smith SL, Locke JL, Bennett J. Reduced speaking rate as an early predictor of reading disability. American Journal of Speech-Language Pathology. 2006 Aug 1;15(3):289–97. pmid:16896178
  22. 22. Smith AB, Lambrecht-Smith S, Locke JL, Bennett J. A longitudinal study of speech timing in young children later found to have reading disability. Journal of Speech, Language, and Hearing Research. 2008 Oct 1;51(5):1300–14. pmid:18812490
  23. 23. Keren-Portnoy T, Vihman MM, DePaolis RA, Whitaker CJ, Williams NM. The role of vocal practice in constructing phonological working memory. Journal of Speech, Language, and Hearing Research. 2010 Oct 1;53(5):1280–93.24. pmid:20631231
  24. 24. Stoel-Gammon C. Sounds and words in early language acquisition: The relationship between lexical and phonological development. Exploring the speech-language connection. 1998;8:25–52.
  25. 25. Vihman MM, Greenlee M. Individual differences in phonological development: Ages one and three years. Journal of Speech, Language, and Hearing Research. 1987 Dec 1;30(4):503–21.
  26. 26. Bleile K. M. (2006). The late eight. Plural Publishing.
  27. 27. Shriberg LD. Four new speech and prosody-voice measures for genetics research and other studies in developmental phonological disorders. Journal of Speech, Language, and Hearing Research. 1993 Feb 1;36(1):105–40.
  28. 28. Elbro C. Early linguistic abilities and reading development: A review and a hypothesis. Reading and Writing. 1996 Dec 1;8(6):453–85.
  29. 29. Scarborough HS, Brady SA. Toward a common terminology for talking about speech and reading: A glossary of the “phon” words and some related terms. Journal of Literacy Research. 2002 Sep;34(3):299–336.30.
  30. 30. Sutherland D., & Gillon G. T. (2005). Assessment of phonological representations in children with speech impairment. Language, speech, and hearing services in schools, 36(4), 294–307. pmid:16389702
  31. 31. Catts HW, Kamhi AG. The linguistic basis of reading disorders: Implications for the speech-language pathologist. Language, Speech, and Hearing Services in Schools. 1986 Oct 1;17(4):329–41.
  32. 32. Larrivee LS, Catts HW. Early reading achievement in children with expressive phonological disorders. American Journal of Speech-Language Pathology. 1999 May 1;8(2):118–28.
  33. 33. Boada R, Pennington BF. Deficient implicit phonological representations in children with dyslexia. Journal of experimental child psychology. 2006 Nov 1;95(3):153–93. pmid:16887140
  34. 34. Foy JG, Mann V. Does strength of phonological representations predict phonological awareness in preschool children?. Applied Psycholinguistics. 2001 Sep;22(3):301–25.
  35. 35. Swan D, Goswami U. Phonological awareness deficits in developmental dyslexia and the phonological representations hypothesis. Journal of experimental child psychology. 1997 Jul 1;66(1):18–41. pmid:9226932
  36. 36. Apel K. The acquisition of mental orthographic representations for reading and spelling development. Communication Disorders Quarterly. 2009 Nov;31(1):42–52.
  37. 37. Wolter JA, Apel K. Initial acquisition of mental graphemic representations in children with language impairment. Journal of Speech, Language, and Hearing Research. 2010 Feb 1;53(1):179–95. pmid:20150408
  38. 38. McBride-Chang C. The ABCs of the ABCs: The development of letter-name and letter-sound knowledge. Merrill-Palmer Quarterly (1982-). 1999 Apr 1:285–308.
  39. 39. Treiman R, Weatherston S, Berch D. The role of letter names in children's learning of phoneme–grapheme relations. Applied Psycholinguistics. 1994 Jan;15(1):97–122.
  40. 40. Share DL. Knowing letter names and learning letter sounds: A causal connection. Journal of experimental child psychology. 2004 Jul 1;88(3):213–33. pmid:15203298
  41. 41. Treiman R, Tincoff R, Rodriguez K, Mouzaki A, Francis DJ. The foundations of literacy: Learning the sounds of letters. Child Development. 1998 Dec 1;69(6):1524–40. pmid:9914638
  42. 42. Share DL. Phonological recoding and self-teaching: Sine qua non of reading acquisition. Cognition. 1995 May 1;55(2):151–218. pmid:7789090
  43. 43. Share DL. Orthographic learning at a glance: On the time course and developmental onset of self-teaching. Journal of experimental child psychology. 2004 Apr 1;87(4):267–98. pmid:15050455
  44. 44. Burgess SR, Lonigan CJ. Bidirectional relations of phonological sensitivity and prereading abilities: Evidence from a preschool sample. Journal of Experimental Child Psychology. 1998 Aug 1;70(2):117–41. pmid:9729452
  45. 45. Saletta M, Goffman L, Brentari D. Reading skill and exposure to orthography influence speech production. Applied psycholinguistics. 2016 Mar;37(2):411–34. pmid:27057073
  46. 46. Chall JS, Read LT. The great debate. New York, NY: McGraw-Hill; 1967.
  47. 47. De Jong PF, Olson RK. Early predictors of letter knowledge. Journal of experimental child psychology. 2004 Jul 1;88(3):254–73. pmid:15203300
  48. 48. de Jong PF, van der Leij A. Specific contributions of phonological abilities to early reading acquisition: Results from a Dutch latent variable longitudinal study. Journal of Educational Psychology. 1999 Sep;91(3):450.
  49. 49. Evans MA, Bell M, Shaw D, Moretti S, Page J. Letter names, letter sounds and phonological awareness: An examination of kindergarten children across letters and of letters across children. Reading and writing. 2006 Dec 1;19(9):959–89.
  50. 50. Lonigan CJ, Burgess SR, Anthony JL. Development of emergent literacy and early reading skills in preschool children: evidence from a latent-variable longitudinal study. Developmental psychology. 2000 Sep;36(5):596. pmid:10976600
  51. 51. Scarborough HS. Early identification of children at risk for reading disabilities: Phonological awareness and some other promising predictors. Specific reading disability: A view of the spectrum. 1998:75–119.
  52. 52. Share DL, Jorm AF, Maclean R, Matthews R. Sources of individual differences in reading acquisition. Journal of educational Psychology. 1984 Dec;76(6):1309.
  53. 53. Snowling MJ, Gallagher A, Frith U. Family risk of dyslexia is continuous: Individual differences in the precursors of reading skill. Child development. 2003 Mar 1;74(2):358–73. pmid:12705560
  54. 54. Dinnsen DA, Chin SB, Elbert M. On the lawfulness of change in phonetic inventories. Lingua. 1992 Mar 1;86(2–3):207–22.
  55. 55. Locke JL. Babbling and early speech: Continuity and individual differences. First Language. 1989 Jan;9(6):191–205.
  56. 56. Stoel-Gammon C. (1985). Phonetic inventories, 15–24 months: A longitudinal study. Journal of Speech, Language, and Hearing Research, 28(4), 505–512. Nip IS, Green JR, Marx DB. The co-emergence of cognition, language, and speech motor control in early development: A longitudinal correlation study. Journal of Communication Disorders. 2011 Mar 1;44(2):149–60.
  57. 57. Oller DK. The emergence of the sounds of speech in infancy In Yeni-Komshian GH, Kavanagh JF, & Ferguson CA (Eds.), Child phonology (Vol. 1, pp. 93–112).
  58. 58. Oller DK. The emergence of the speech capacity. Psychology Press; 2000 Jan 1.
  59. 59. Oller DK, Eilers RE, Neal AR, Schwartz HK. Precursors to speech in infancy: The prediction of speech and language disorders. Journal of communication disorders. 1999 Jul 1;32(4):223–45. pmid:10466095
  60. 60. McGillion M, Herbert JS, Pine J, Vihman M, DePaolis R, Keren-Portnoy T, et al. What paves the way to conventional language? The predictive value of babble, pointing, and socioeconomic status. Child development. 2017 Jan 1;88(1):156–66. pmid:27859008
  61. 61. American Speech-Language-Hearing Association. Guidelines for audiologic screening. 1997.
  62. 62. McCathren RB, Yoder PJ, Warren SF. The relationship between prelinguistic vocalization and later expressive vocabulary in young children with developmental delay. Journal of Speech, Language, and Hearing Research. 1999 Aug 1;42(4):915–24. pmid:10450911
  63. 63. Oller DK, Eilers RE, Urbano R, Cobo-Lewis AB. Development of precursors to speech in infants exposed to two languages. Journal of child language. 1997 Jun;24(2):407–25. pmid:9308425
  64. 64. Whitehurst GJ, Smith M, Fischel JE, Arnold DS, Lonigan CJ. The continuity of babble and speech in children with specific expressive language delay. Journal of Speech, Language, and Hearing Research. 1991 Oct 1;34(5):1121–9.
  65. 65. Justice LM, Pence K, Bowles RB, Wiggins A. An investigation of four hypotheses concerning the order by which 4-year-old children learn the alphabet letters. Early Childhood Research Quarterly. 2006 Jul 1;21(3):374–89.
  66. 66. Woodcock RW. Woodcock reading mastery tests, revised. Circle Pines, MN: American Guidance Service; 1987.
  67. 67. Hustad KC, Allison K, McFadd E, Riehle K. Speech and language development in 2-year-old children with cerebral palsy. Developmental neurorehabilitation. 2014 Jun 1;17(3):167–75. pmid:23627373
  68. 68. Hoffman L. (2015). Longitudinal analysis: Modeling within-person fluctuation and change. New York, NY: Routledge Academic.
  69. 69. Miller CA, Kail R, Leonard LB, Tomblin JB. Speed of processing in children with specific language impairment. Journal of Speech, Language, and Hearing Research. 2001 Apr 1;44(2):416–33. pmid:11324662
  70. 70. Cain K, Oakhill J. Matthew effects in young readers: Reading comprehension and reading experience aid vocabulary development. Journal of learning disabilities. 2011 Sep;44(5):431–43. pmid:21772058
  71. 71. Duff D, Tomblin JB, Catts H. The influence of reading on vocabulary growth: A case for a Matthew Effect. Journal of Speech, Language, and Hearing Research. 2015 Jun 1;58(3):853–64. pmid:25812175
  72. 72. Catts HW, Herrera S, Nielsen DC, Bridges MS. Early prediction of reading comprehension within the simple view framework. Reading and Writing. 2015 Nov 1;28(9):1407–25.