https://orcid.org/0000-0003-1859-5623
Figures
Abstract
Daily-life conversation relies on speech perception in quiet and noise. Because of the COVID-19 pandemic, face masks have become mandatory in many situations. Acoustic attenuation of sound pressure by the mask tissue reduces speech perception ability, especially in noisy situations. Masks also can impede the process of speech comprehension by concealing the movements of the mouth, interfering with lip reading. In this prospective observational, cross-sectional study including 17 participants with normal hearing, we measured the influence of acoustic attenuation caused by medical face masks (mouth and nose protection) according to EN 14683 and of N95 masks according to EN 1149 (EN 14683) on the speech recognition threshold and listening effort in various types of background noise. Averaged over all noise signals, a surgical mask significantly reduced the speech perception threshold in noise was by 1.6 dB (95% confidence interval [CI], 1.0, 2.1) and an N95 mask reduced it significantly by 2.7 dB (95% CI, 2.2, 3.2). Use of a surgical mask did not significantly increase the 50% listening effort signal-to-noise ratio (increase of 0.58 dB; 95% CI, 0.4, 1.5), but use of an N95 mask did so significantly, by 2.2 dB (95% CI, 1.2, 3.1). In acoustic measures, mask tissue reduced amplitudes by up to 8 dB at frequencies above 1 kHz, whereas no reduction was observed below 1 kHz. We conclude that face masks reduce speech perception and increase listening effort in different noise signals. Together with additional interference because of impeded lip reading, the compound effect of face masks could have a relevant impact on daily life communication even in those with normal hearing.
Citation: Rahne T, Fröhlich L, Plontke S, Wagner L (2021) Influence of surgical and N95 face masks on speech perception and listening effort in noise. PLoS ONE 16(7): e0253874. https://doi.org/10.1371/journal.pone.0253874
Editor: Amitava Mukherjee, VIT University, INDIA
Received: March 23, 2021; Accepted: June 14, 2021; Published: July 1, 2021
Copyright: © 2021 Rahne et al. 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.
Data Availability: All relevant data are within the manuscript and its Supporting information files.
Funding: The authors received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Coronavirus disease 2019 (COVID-19) is caused by infection with the novel SARS-CoV-2 coronavirus. The disease, which primarily affects the respiratory tract, was first described in Wuhan, China, at the end of 2019. It developed into an epidemic in that country in January 2020 and was declared a pandemic in March 2020. Evidence supports the potential for transmission in superspreading events [1, 2], and infection usually occurs through transmission of droplets. Aerosol transmission is possible, especially in closed, poorly ventilated rooms [3, 4].
In many countries, including Germany and the United States of America, regulatory bodies such as the Robert Koch Institute (RKI) and the U.S. Centers for Disease Control and Prevention (CDC) call for the use of mouth and nose protection or even a medical face mask [5] to reduce infection risk. For many social situations, local or regional rules have made this use mandatory.
Use of masks can raise obstacles to communication [6]. Masks cover the mouth, which impedes the gain of information from lip and facial movements to support aural comprehension. Lip reading is especially important for hearing in noisy situations because these movements provide temporal clues and increase awareness of the language elements. Particularly, information about spoken consonants is provided [7]. Access to lip reading cues has a positive effect on speech understanding in background noise, especially for people who have hearing impairment [8]. The speech reception threshold (SRT) in background noise can be improved by 3–5 dB if the face is visible [9, 10].
Masks also can affect the acoustic properties of the speech signal itself, with negative implications for speech perception. Simple medical masks, such as those used in operating rooms, reduce the spoken language level by 3–4 dB in the high-frequency range of 2000–7000 Hz, and N95 masks reduce the level by about 12 dB [11]. A decrease of 12 dB has also been measured for surgical masks [12]. Although speaking through face masks alters the speech signal, some specific features of voice quality features (e.g., harmonic-to-noise ratio, temporal pattern) remain largely unaffected [13]. The sound pressure level of spoken language is reduced mainly at frequencies ≥2000 Hz, so that the resulting signal would be similar to that for listeners having who have slight high-frequency hearing loss [14]. In a recent study of health workers, the speech perception threshold was increased by 12.4 dB if the speaker used an N95 mask and a face shield [15].
In listeners with normal hearing, face masks do not significantly affect speech intelligibility in noise [6, 16, 17]. However, studies making this assessment did not measure the speech perception threshold (SRT) in noise, i.e., the signal-to-noise ratio (SNR) that results in 50% speech recognition. SRT is widely used in the clinical routine as a sensitive diagnostic tool. In this study, we used the Oldenburg sentence test (OLSA), which presents lists of unpredictable five-word sentences (name—verb—numeral—adjective—object) to the listener in a background masker with different SNRs [18]. Depending on the number of words (score) correctly repeated by the listener, the SNR is increased or decreased for the subsequent sentence presentation. The adaptive procedure converges on the SNR that corresponds to a 50% correct score, defined as SRT50.
Even a little signal level attenuation, as expected with the use of face masks, would affect hearing in a noisy situation with SNRs close to the individual SRT. In hearing-impaired listeners, speech perception in noise would be more affected by face mask–related declines in sound pressure levels because of the reduced amplification by the outer hair cells, resulting in a smaller dynamic range [6, 8, 19, 20]. In hearing-impaired individuals, speech perception in noise also is reduced to an increased pure-tone threshold in quiet [21, 22], mainly attributable to a reduced cochlear amplifier function. Other than in conductive hearing loss, hearing aids can compensate only partially for impaired hair cell function. Therefore, reduced speech level in noise would particularly affect daily life communication in hearing-impaired listeners. For such “pseudo hearing impairment”, hearing amplifiers that compensate for these deficits are required, especially for vulnerable occupational groups and social groups [23]. Specific recommendations for educational settings also aim to compensate for the negative aspects of mouth and nose protection [24].
Active listening requires cognitive resources such as focus and attention. A decreased speech level in quiet or SNR in noisy situations increases stress on cognitive resources [25]. The use of mouth and nose protection also is expected to increase the listening effort, which in turn has consequences for many daily life situations with a high communication load, such as a classroom. With the Adaptive CAtegorical Listening Effort Scaling Test (ACALES), a clinical procedure has recently become available that enables measurement of the listening effort amid background noise [26]. Sentences are presented in a background masker with different SNRs. Subjective listening effort is quantified on a 14-point subjective categorical scale ranging from ‘no effort’ to ‘extreme effort’. SNRs of ACALES are adaptively adjusted for each presentation based on previous ratings, to individually cover the entire range of possible categories and to finally determine the absolute threshold (SNRcut) with a moderate listening effort [27, 28].
The aim of this research project was to measure the influence of acoustic attenuation caused by medical face masks (mouth and nose protection) according to EN 14683 and N95 masks according to EN 1149 (EN 14683) on the speech recognition threshold and the listening effort in various types of background noise in listeners with normal hearing.
Materials and methods
This observational, cross-sectional study was conducted with adult volunteers with normal hearing. All participants were recruited by personnel contacts of the authors between November 2020 and January 2021 in Halle (Saale), Germany. Inclusion criteria were a minimum age of 18 years, normal hearing bilaterally, and fluency in German language (native speakers). Normal hearing was confirmed if the bilateral pure-tone thresholds for air conduction were ≤15 dB hearing level (HL) at both ears at the frequencies of 0.5, 1, 2, and 4 kHz. Audiological assessments were conducted using an AT900 audiometer (Auritec, Hamburg, Germany). Exclusion criteria were pregnancy and not meeting the inclusion criteria. This sample is considered representative of a larger population with normal hearing. Informed written consent was obtained from all participants for study inclusion. The study took place at the Audiology Lab of the University Hospital Halle (Saale), Germany, and was approved by the ethics committee of the Medical Faculty of the Martin Luther University Halle-Wittenberg (approval number 2020–160) and conducted in accordance with the Declaration of Helsinki.
The influence of face masks on speech perception in noise was measured in a sound-attenuating booth using Foliodress LOOP TYPE IIR surgical face masks (CMC Medical Devices & Drugs, Malaga, Spain), according to European standard EN 14683 (‘surgical mask’), and RSN95B FFP2 NR particle-filtering half masks (Rysam Medical Equipment Manufacturing, Donguan City, China) (‘N95 mask’; Fig 1).
To measure the effects of masks on the acoustic features of the speech test signals, the masks were placed directly before the grid of a loudspeaker (CD 1020, Canton, Weilrod, Germany) which was positioned 1 m in front of a “dummy head” (KU 100, Neumann, Berlin, Germany; Fig 1). The olnoise speech simulation noise for a male speaker (‘Olnoise male’) and a female speaker (‘Olnoise female’) and the International Speech Test Signal (ISTS) [29] were presented for 30 s at a sound pressure level of 65 dB, using the Oldenburger Measurement Application 2.2 R&D software (Hörtech, Oldenburg, Germany), a Gigaport eX audio interface (ESI Audiotechnik, Leonberg, Germany), and a PLMRA400 amplifier (Pyle, Brooklyn, NY, USA). White noise was also presented for comparison. Sound signals were recorded by the microphones of the dummy head and amplified by a Fireface 400 audio interface (RME, Haimhausen, Germany). The root-mean-square (RMS) of the recording and the 1/3-octave amplitude spectrum were computed for every experimental condition by using Python scripts. The magnitude spectra of the two channels were averaged, and differences between recordings with and without masks were computed.
In listeners with normal hearing, speech recognition in noise was measured with the German Matrix Sentence Test OLSA (Hörtech, Oldenburg, Germany) using the same setup. Noise signals were continuously presented from behind at a sound pressure level of 60 dB. After two training runs, lists of 20 sentences were superposed and presented frontally (S0N180). The sound pressure level of every sentence was adjusted based on the participant’s response to the previous sentence to measure the open-set SRT for 50% correct recognition (SRT50) as the primary endpoint. ACALES v2.2 software (Hörtech, Oldenburg, Germany) was used to measure the listening effort. After two training runs, a series of two consecutive sentences with various SNRs from the OLSA in a 60-dB SPL background noise from a rear loudspeaker (S0N180) were frontally presented to the participants. Listening effort was measured in 14 effort categorical units (ecu). The speech level changed adaptively between −40 dB SNR and +20 dB SNR, based on the previous assessment of the subjectively perceived listening effort. Secondary endpoints were the SNRcut, i.e., the SNR at 7 ecu, and the slopes of the SNR-effort function for SNR with listening effort >7 ecu (mlow) and <7 ecu (mhigh).
All participants were seated while the head was fixed using a Papillon head fixation system (Focal Meditech, Tilburg, The Netherlands; Fig 1). After two OLSA and two ACALES training runs, all participants completed the test runs while the noise signals (Olnoise female, Olnoise male, ISTS) and mask conditions (no mask, surgical mask, N95 mask) were applied in pseudorandomized order.
Primary and secondary endpoints were descriptively analyzed and tested for normality using the Shapiro–Wilk test. We then compared the distributions of SRT50 and SNRcut with the results of an analysis of variance (ANOVA) for repeated measures using the within-subject factors of noise type (Olnoise female, Olnoise male, ISTS) and mask type (w/o mask, surgical mask, N95 mask). The assumption of sphericity was tested using Mauchly’s test, and we adjusted degrees of freedom with Bonferroni correction for all post-hoc comparisons. SPSS software version 25 (IBM, Ehningen, Germany) was used for statistical analyses.
Results
Seventeen participants with normal hearing (14 female, 3 male) were included in the study. Their average age was 28 years (standard deviation [SD] = 5.4). The average pure-tone thresholds across participants and frequencies (4PTA0.5-4kHz) were 7.0 dB HL (SD = 2.8) for the left ear and 6.3 dB HL (SD = 3.2) for the right ear.
Table 1 shows the descriptive data for the OLSA hearing-in-noise test and the ACALES listening effort test. Fig 2 shows the SRT50 distributions for all applied mask type conditions and noise signals. Mauchly’s test indicated that the assumption of sphericity was violated for the SRT50 (noise type: χ2(2) = 20.1, p < 0.001; mask type × noise type interaction: χ2(9) = 19.6, p < 0.05), so degrees of freedom were corrected using Greenhouse–Geisser estimates of sphericity. An ANOVA with noise type and mask type as within-subjects factors revealed main effects of mask type (F(1.9, 29.8) = 74.9, p < 0.001) and of noise type (F(1.2, 18.4) = 459.9, p < 0.001). We found no interaction between mask type and noise type (F(2.7, 42.5) = 1.2, p > 0.1). Post-hoc comparisons showed that a surgical mask increased the SRT50 significantly, by 1.6 dB (standard error [SE] = 0.21, confidence interval [CI] = 1.0, 2.1) on average. A further significant increase of 1.1 dB (SE = 0.25, CI = 0.5, 1.8) was detected with use of an N95 mask, resulting in a total increase of 2.7 dB (SE = 0.20, CI = 2.2, 3.2) across all noise signals. SRT50 was lowest for the ISTS (M = -21.8 dB, SE = 0.74), followed by the Olnoise female (M = -11.7 dB, SE = 0.35) and the Olnoise male (M = -9.7 dB, SE = 0.74) across all mask types.
Putting a surgical face mask or an N95 mask between the speaker and the listener reduced the performance, as reflected by an increased SRT50. Boxes show the 25th, 50th and 75th percentiles, and whiskers indicate the 5th and 95th percentiles. Asterisks (*) indicate significant differences (p < 0.05).
Fig 3 shows the SRTcut distributions for all mask type conditions and noise signals. Mauchly’s test indicated that the assumption of sphericity had not been violated for the SNRcut (p > 0.1). An ANOVA with noise type and mask type as within-subjects factors revealed main effects of mask type (F(2, 32) = 16.3, p < 0.001) and noise type (F(1, 16) = 132.5, p < 0.001). We found no interaction between mask type and noise type (F(2, 32) = 1.5, p > 0.1). Post-hoc comparisons showed that a surgical mask did not affect SRTcut significantly, with an increase of 0.58 dB (SE = 0.35, CI = 0.4, 1.5) on average. A further significant increase of 1.6 dB (SE = 0.46, CI = 0.4, 2.8) was measured with use of an N95 mask, resulting in a significant total increase of 2.2 dB (SE = 0.37, CI = 1.2, 3.1) across both noise signals. SRTcut was lower for the ISTS (M = -12.6 dB, SE = 1.4), compared to the Olnoise male (M = -5.4 dB, SE = 0.81) across all mask types.
Boxes show the 25th, 50th and 75th percentiles, and whiskers indicate the 5th and 95th percentiles. Asterisks (*) indicate significant differences (p < 0.05).
Fig 4A shows the 1/3-octave amplitude spectra of the dummy head recording for white noise, ISTS, Olnoise female, and Olnoise male noise signals presented without a mask. Fig 4B shows the differences between recordings without a mask and with a surgical mask or an N95 mask for all used noise signals. The amplitude reduction was comparable among all noise signals. At low frequencies below 1 kHz, no reduction was observed. At frequencies above 1 kHz, the mask tissue reduced the amplitudes by up to 8 dB. Although a surgical mask reduced the amplitudes at frequencies above 2 kHz, a reduction was already measurable at frequencies above 1 kHz with the N95 mask. For the surgical mask, maximum amplitude reduction was observed at 8 kHz. Amplitude reduction with an N95 mask showed two local maxima in 1/3-octave frequency bands at 2.520 Hz and at 5.040 Hz. A surgical mask reduced the RMS of the white noise dummy head recordings by 2.6 dB and the N95 mask reduced the RMS by 4.0 dB. Only limited RMS reduction (< 0.2 dB) was measured for all speech-noise signals.
A. 1/3-octave amplitude spectra of the dummy head recordings for white noise (black line), ISTS (dashed line), Olnoise female (red line), and Olnoise male (blue line) noise signals presented without a mask. B. Spectral differences in recordings with a surgical mask (bold lines) or an N95 mask (thin lines) for all used noise signals compared with the unmasked recordings. At frequencies above 1 kHz, some mask tissue reduced the amplitudes by up to 8 dB. A surgical mask reduced the amplitudes at frequencies above 2 kHz, but a reduction was already measurable at frequencies already above 1 kHz for the N95 mask.
Discussion
We found that speech perception in noise was significantly reduced if a medical face mask was placed between the speech source and the listener. Reductions were even steeper with the use of an N95 mask. This effect is comparable to the reductions reported by Goldin et al. [11], Branda [12], and Bandaru et al. [15]. Those studies used live voice, whereas studies using audio-only recordings made with medical masks showed no significant effects on speech intelligibility [6, 16, 17].
The current findings indicate that a face mask induced SRT reductions consistently across three different noise types. Without a mask between the speaker and the listener, the measured SRT was better than the normative range of the used speech-in-noise test for signal and noise both presented from the front (S0N0) [18]. Use of masks still resulted in SRTs being within that reference value range. In daily life situations, the noise source is not fixed and would potentially be in front of the listener in many scenarios. In such cases, the baseline SRT would already be worse as compared to the used configuration with noise from behind (S0N180). Because only the speech signals would be affected by a face mask in those situations, speech-in-noise perception SRTs would be even lower, i.e., worse, than measured in the present study.
The surgical and N95 masks attenuated acoustic transmission of sound. Dummy-head recordings showed RMS reductions in speech noise signals in the same low magnitude as previously reported [6, 8]. For white noise, however, our results showed a larger RMS reduction for both mask types. A more detailed analysis of the amplitude spectrum indicated no relevant amplitude reduction for low frequencies but a pronounced reduction for higher frequencies. Both mask types modified sound transmission like a low-pass filter with cut-off frequencies of 2 kHz for the surgical mask and 1 kHz for the N95 mask. The measured magnitude of the attenuation, however, was comparable between the surgical and N95 masks and in the same range as [11] or greater than previously measured [14]. Our results strengthen previous findings by providing attenuation data for currently used medical mask types. Even if acoustic attenuation is linear and independent of the specific acoustic signals, we nevertheless detected differences among the noise types and ISTS. The minor differences could be attributed to the presentation of randomly selected different parts of the recorded clinical test materials. Therefore, the ISTS signals particularly would have differed between the specific attenuation experiments.
The observed increase in the speech-perception-in-noise threshold was below the measured attenuation of sound pressure by the masks. Because SRT is based on the difference between speech and noise sound pressure levels, an attenuation of the speech signal alone would potentially increase the SRT by the same amount. The acoustic attenuation, however, was not equal across the frequency spectrum. The low, middle, and high frequencies contributed differently to speech perception, so the observed discrepancy between acoustical sound attenuation and SRT increase is plausible.
Acoustic attenuation and reduced speech perception in noise have possible implications for communications in daily life. Because normative reference data are available for the speech-in-noise tests we used, our findings allow for a quantification of the effect. In listeners with normal hearing, the slope of the speech intelligibility function at the SRT is 17.1%/dB [18]. If the observed SRT shift of 1.58 dB (surgical mask) or 2.69 dB (N95 mask) were caused only by a mask-related speech level reduction, the estimated speech intelligibility decrease would be 27 percentage points for the surgical mask and 46 percentage points for an N95 mask used with that specific constant SNR.
The results show a greater listening effort in noise if an N95 face mask is placed between the speaker and the listener. The face masks increased the absolute threshold (SNRcut) of the hearing effort function, which reflects increased listening effort. The observed effect of the masks on listening effort was of the same magnitude as the speech perception threshold in noise. Low-pass filtering, such as that caused by face masks, would be expected to reduce the audibility of high-frequency portions of spoken speech, such as consonants and sibilants, or at least to increase the listening effort. Face masks increase the effort that the speaker must make, as well, with symptoms of vocal fatigue, discomfort, and coordination of speech and breathing [30]. To the listener, an increased communication effort would therefore be expected because of the face masks and alter the interpersonal components of communication.
In the present study, the face mask types were visible to the listeners, which could have biased their speech perception performance. The reported acoustic measurements, however, showed a clear modification of speech signals, which can explain the observed subjective results. An influence of habituation and training on the SRT of the matrix test we used was reduced by randomizing the experimental conditions and starting with training lists. In daily life, the sight of a speaker who uses a face mask could potentially increase the subjective perceived listening effort because of the missing lip reading and other visual, e.g. emotional cues of face-to-face communication. The results of this study, however, showed no evidence for an increased listening effort because of the masks alone.
Conclusions
We conclude that face masks modify acoustic features of speech signals and significantly reduce speech perception in different noise signals. In combination with an additional reduction because of missing lip reading, the combined effect of face masks would be expected to have a relevant impact on daily life communication.
References
- 1. Bouffanais R, Lim SS. Cities—try to predict superspreading hotspots for COVID-19. Nature. 2020; 583:352–5. pmid:32651472.
- 2. Correa-Martínez CL, Kampmeier S, Kümpers P, Schwierzeck V, Hennies M, Hafezi W, et al. A Pandemic in Times of Global Tourism: Superspreading and Exportation of COVID-19 Cases from a Ski Area in Austria. J Clin Microbiol. 2020; 58. pmid:32245833.
- 3. Anderson EL, Turnham P, Griffin JR, Clarke CC. Consideration of the Aerosol Transmission for COVID-19 and Public Health. Risk Anal. 2020; 40:902–7. Epub 2020/05/01. pmid:32356927.
- 4. Sommerstein R, Fux CA, Vuichard-Gysin D, Abbas M, Marschall J, Balmelli C, et al. Risk of SARS-CoV-2 transmission by aerosols, the rational use of masks, and protection of healthcare workers from COVID-19. Antimicrob Resist Infect Control. 2020; 9:100. Epub 2020/07/06. pmid:32631450.
- 5. Carr E. Through a Face Mask. Clin J Oncol Nurs. 2020; 24:345. pmid:32678363.
- 6. Mendel LL, Gardino JA, Atcherson SR. Speech understanding using surgical masks: a problem in health care. J Am Acad Audiol. 2008; 19:686–95. pmid:19418708.
- 7. Moradi S, Lidestam B, Danielsson H, Ng EHN, Rönnberg J. Visual Cues Contribute Differentially to Audiovisual Perception of Consonants and Vowels in Improving Recognition and Reducing Cognitive Demands in Listeners With Hearing Impairment Using Hearing Aids. J Speech Lang Hear Res. 2017; 60:2687–703. pmid:28651255.
- 8. Atcherson SR, Mendel LL, Baltimore WJ, Patro C, Lee S, Pousson M, et al. The Effect of Conventional and Transparent Surgical Masks on Speech Understanding in Individuals with and without Hearing Loss. J Am Acad Audiol. 2017; 28:58–67. pmid:28054912.
- 9. Grange JA, Culling JF. The benefit of head orientation to speech intelligibility in noise. J Acoust Soc Am. 2016; 139:703–12. pmid:26936554.
- 10. Middelweerd MJ, Plomp R. The effect of speechreading on the speech-reception threshold of sentences in noise. J Acoust Soc Am. 1987; 82:2145–7. pmid:3429736.
- 11. Goldin A, Weinstein B, Shiman N. How Do Medical Masks Degrade Speech Reception. Hear Rev. 2020; 27:8–9. Available from: https://www.hearingreview.com/hearing-loss/health-wellness/how-do-medical-masks-degrade-speech-reception.
- 12.
Branda E. Improving communication with face masks. Signia White Paper. 2020.
- 13. Magee M, Lewis C, Noffs G, Reece H, Chan JCS, Zaga CJ, et al. Effects of face masks on acoustic analysis and speech perception: Implications for peri-pandemic protocols. J Acoust Soc Am. 2020; 148:3562. pmid:33379897.
- 14. Corey RM, Jones U, Singer AC. Acoustic effects of medical, cloth, and transparent face masks on speech signals. J Acoust Soc Am. 2020; 148:2371. pmid:33138498.
- 15. Bandaru SV, Augustine AM, Lepcha A, Sebastian S, Gowri M, Philip A, et al. The effects of N95 mask and face shield on speech perception among healthcare workers in the coronavirus disease 2019 pandemic scenario. J Laryngol Otol. 2020:1–4. Epub 2020/09/28. pmid:32981539.
- 16. Palmiero AJ, Symons D, Morgan JW, Shaffer RE. Speech intelligibility assessment of protective facemasks and air-purifying respirators. J Occup Environ Hyg. 2016; 13:960–8. pmid:27362358.
- 17. Thomas F, Allen C, Butts W, Rhoades C, Brandon C, Handrahan DL. Does wearing a surgical facemask or N95-respirator impair radio communication. Air Med J. 2011; 30:97–102. pmid:21382570.
- 18. Wagener K, Brand T, Kollmeier B. Development and evaluation of a German sentence test—Part III: Evaluation of the Oldenburg sentence test. Z Audiol. 1999; 38:86–95.
- 19. Schuster M, Arias-Vergara T, Müller-Hörner R, Winterholler C, Bocklet T. „Verstehen mich mit der Maske noch alle?“: Coronavirus-Pandemie. MMW Fortschr Med. 2020; 162:42–4. pmid:32780377.
- 20. Sharif SP, Blagrove E. COVID-19, masks and communication in the operating theatre: the importance of face value. Psychol Med. 2020:1–4. pmid:32938519.
- 21. Carhart R, Tillman TW. Interaction of competing speech signals with hearing losses. Arch Otolaryngol. 1970; 91:273–9. pmid:5414080.
- 22. Plomp R. Auditory handicap of hearing impairment and the limited benefit of hearing aids. J Acoust Soc Am. 1978; 63:533–49. pmid:670550.
- 23. Chodosh J, Weinstein BE, Blustein J. Face masks can be devastating for people with hearing loss. BMJ. 2020; 370:m2683. pmid:32646862.
- 24. Nobrega M, Opice R, Lauletta MM, Nobrega CA. How face masks can affect school performance. Int J Pediatr Otorhinolaryngol. 2020; 138:110328. pmid:32920450.
- 25. Peelle JE. Listening Effort: How the Cognitive Consequences of Acoustic Challenge Are Reflected in Brain and Behavior. Ear Hear. 2018; 39:204–14. pmid:28938250.
- 26. Krueger M, Schulte M, Brand T, Holube I. Development of an adaptive scaling method for subjective listening effort. J Acoust Soc Am. 2017; 141:4680. pmid:28679238.
- 27. Luts H, Eneman K, Wouters J, Schulte M, Vormann M, Buechler M, et al. Multicenter evaluation of signal enhancement algorithms for hearing aids. J Acoust Soc Am. 2010; 127:1491–505. pmid:20329849.
- 28. Krueger M, Schulte M, Zokoll MA, Wagener KC, Meis M, Brand T, et al. Relation Between Listening Effort and Speech Intelligibility in Noise. Am J Audiol. 2017; 26:378–92. pmid:29049622.
- 29. Holube I, Fredelake S, Vlaming M, Kollmeier B. Development and analysis of an International Speech Test Signal (ISTS). Int J Audiol. 2010; 49:891–903. pmid:21070124.
- 30. Ribeiro VV, Dassie-Leite AP, Pereira EC, Santos ADN, Martins P, Irineu RdA. Effect of Wearing a Face Mask on Vocal Self-Perception during a Pandemic. J Voice. 2020. Epub 2020/09/30. pmid:33011037.