Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Acoustic voice characteristics with and without wearing a facemask

Abstract

Facemasks are essential for healthcare workers but characteristics of the voice whilst wearing this personal protective equipment are not well understood. In the present study, we compared acoustic voice measures in recordings of sixteen adults producing standardised vocal tasks with and without wearing either a surgical mask or a KN95 mask. Data were analysed for mean spectral levels at 0–1 kHz and 1–8 kHz regions, an energy ratio between 0–1 and 1–8 kHz (LH1000), harmonics-to-noise ratio (HNR), smoothed cepstral peak prominence (CPPS), and vocal intensity. In connected speech there was significant attenuation of mean spectral level at 1–8 kHz region and there was no significant change in this measure at 0–1 kHz. Mean spectral levels of vowel did not change significantly in mask-wearing conditions. LH1000 for connected speech significantly increased whilst wearing either a surgical mask or KN95 mask but no significant change in this measure was found for vowel. HNR was higher in the mask-wearing conditions than the no-mask condition. CPPS and vocal intensity did not change in mask-wearing conditions. These findings implied an attenuation effects of wearing these types of masks on the voice spectra with surgical mask showing less impact than the KN95.

Introduction

Facemasks are an essential piece of personal protective equipment (PPE) and can be broadly categorized into respirators, medical masks (including surgical masks and procedure masks), and woven fabric (cloth) masks1. Respirators and surgical masks provide different levels of barrier to prevent infectious transmission via aerosols and droplets2. Masks with higher barrier levels (e.g. N95) are used in aerosol generating procedures (AGPs) and other high risk activities1. During non-aerosol generating protocols, surgical masks offer a similar degree of protection to N95 masks against viral respiratory infections including coronaviruses in health care workers (HCWs)3. Although surgical masks do not provide the same level of protection as N95 masks, they prevent some aerosols and droplets from being released from phonation and respiratory activities, contributing to reducing the risk of transmission4. In the SARS-CoV2 pandemic (COVID-19), such masks have been recommended for use by not only HCWs but also the general public in areas with known or suspected widespread transmission, high population density, or settings where physical distancing cannot be effectively achieved5. Although masks are effective PPE4, wearing a mask negatively affects the physiological and psychological performance of HCWs6.

Masks also interfere with effective verbal communication. Certain masks particularly the N95 respirators can impact speech understanding by listeners7. Word intelligibility dropped between 1 and 17% while wearing respirators commonly used by HCWs in which N95 mask resulted in a mean (standard deviation, SD) of modified rhyme test (MRT) score of 83 (16.2)% compared to 92 (5.8)% in non-mask controls8. The use of N95 mask in background noise resulted in a significant decrease in speech perception accuracy9. Speaking while wearing a mask at longer distances decreases speech perception accuracy by an even greater magnitude than not wearing a mask10. A mask also physically creates a visual barrier precluding lip reading11, precluding communication cues in people with hearing loss and communication disabilities such as aphasia12. From a user’s perspective, wearing masks increased perception of vocal effort, reduced auditory feedback, and difficult coordination of speech and breathing13. Understanding the aspects of the voice changes whilst wearing a mask is important so clinical decision, making and choice of mask is appropriate to meet infection control and optimal verbal communication.

Although it is believed that facemasks attenuate sound transmission like a low-pass filter10,14, little information is available on voice characteristics whilst wearing a facemask. The scarce literature on the topic suggests possible changes in the speech spectrum. Mendel et al.15 compared speech spectral levels calculated as total root mean square (RMS) power from the Connected Speech Test (CST) stimuli produced by one speaker with and without wearing a surgical mask. They found a significant difference in the total RMS power between the two conditions. However, the affected frequency band was not reported. Atcherson et al.11 found that the total RMS values of speech signals from the CST stimuli were significantly higher when not wearing a mask compared to the conditions with a mask. They also did not mention which frequency range was affected by the mask. Goldin et al.14 utilised a head and torso simulator to play white noise via the model’s mouth without a mask and with a surgical mask and a N95 respirator. They found that facemasks attenuated the sound levels at frequency regions between 2 and 7 kHz by 3–4 dB for the surgical mask and nearly 12 dB for the N95 mask compared with the non-mask condition. Their model lacked natural speech features while its face contour and surface were not similar to human face contour and skin, affecting fitting levels of the masks. However, based on their findings it seems reasonable to hypothesize that mask wearing would attenuate speech spectra at similar frequency bands.

Clear and natural speech production is necessary in accurate speech understanding and requires less listening effort than degraded speech16. Given the widespread use of facemasks in COVID-19 pandemic, it seems reasonable to further clarify the characteristics of the voice signal in speech of vocally healthy speakers who are wearing a mask. Given the above-mentioned findings of the modification of the speech spectra by the mask, the present study quantified the low- and high-frequency energy regions. Spectral analyses not only give information about the overall spectral shape that may be meaningful in speech perception17 but also provide important acoustic correlates of voice quality18. These spectral measures were selected as both the low and high frequency regions also contribute to speech recognition19,20. Low-frequency spectral bands are important in recognizing vowels21,22 and voiced fricative consonants23. High frequency spectral energy makes a significant contribution to speech recognition24,25 including the recognition of vowels26, voiceless and voiced fricative consonants27, spoken and sung text28, and speech recognition in noise29. It has also been shown that the high-frequency region provided perceptual cues for speaker identity30 and gender identification31. In addition, the high frequency region plays an important role in the perception of clear speech: a shift of energy concentration toward higher frequency regions contributes to the clear speech effect for normal-hearing listeners32.

Presumably, the quality and audibility of the voice might also change whilst wearing a facemask as previous studies have observed voice changes in phonation with the mouth covered33. This change may interfere with auditory-perceptual voice judgment by speech language pathologists (SLPs) and ear nose and throat specialists (ENTs). Dysphonic voice quality has also been proven to result in reduced comprehension of speech content by listeners34,35. Wearing a mask may add to this effect by increasing the difficulty of understanding speech of an individual with dysphonic voice. Both voice quality and audibility can be effectively examined using acoustic analysis, which is a non-invasive objective assessment. Traditional acoustic measures of voice quality are based on frequency-based measurements36 and include fundamental frequency (F0)37 and noise (harmonic-to-noise ratio, HNR)38,39,40,41. Amongst these, HNR has been used as a measure of vocal clarity42. The vocal signal can also be analysed based on spectral-based measurement of vowel and connected speech, which does not depend upon reliable tracking of vocal F043. The cepstral peak prominence (CPP) has been shown to have stronger weighted correlations with overall voice quality than other acoustic measures44. Given that it is a measure of periodicity and harmonics strength, a signal with a strong harmonic structure would have a higher CPP than aperiodic signals45. It has been considered a significant predictor of dysphonic severity46. However, there are inherent limitations of cepstral analysis, that is, it is affected by vowel types and vocal intensity47, vocal tasks48, vocal tract49, and the algorithm of software packages48,50. Vocal audibility can be examined both by spectral energy at different frequency bands and sound intensity, which can also be estimated from the acoustic signals.

During the COVID-19 pandemic two types of masks were commercially available in Australia, including standard surgical mask and KN95 mask (China GB2626-2006)51. KN95 mask provided similar protection characteristics to N95 mask52. The major filtering and fitting characteristics of the KN95 mask as provided by 3 M52 were as follows: Filter performance ≥ 95%; Flow rate = 85 L/min; Inhalation resistance ≤ 350 Pa; Exhalation resistance ≤ 250 Pa; and Total inward leakage < 8%. The total inward leakage indicates the amount of an aerosol that enters the mask via both filter penetration and face-seal leakage52. Presumably, the higher barrier level a mask can provide, the greater impact it would have on the voice signals. The aims of the present study were to (1) examine the acoustic characteristics of voice and speech whilst wearing either a surgical mask or a KN95 mask; and (2) compare the acoustic measures between the standard surgical mask and KN95 mask. We hypothesized that: (1) Low- and high-frequency spectral levels, HNR, CPPS, and vocal intensity would change during wearing these facemasks; and (2) Changes in these acoustic measures would be more pronounced with KN95 mask than with standard surgical mask.

Methods

Ethical approval

The voice and speech data analysed in this study was part of a larger project which was approved by the Human Research Ethics Committee of The University of Sydney (protocol number: 2020/399). Informed consent was obtained from all participants to participate in this study. Informed consent to publish was also obtained from a participant for publication of identifying information/image (Fig. 1) in an online open-access publication. The present study was implemented in accordance with relevant ethical guidelines and regulations. The measurement procedures used in this study conformed to the standards set by the latest revision of the Declaration of Helsinki.

Figure 1
figure 1

Voice recording with a KN95 (left) and surgical mask (right).

Participants

Sixteen participants took part in this study (12 females, 4 males) with mean age = 43 years (range = 24–61). All were English speakers, non-smokers, and did not report any voice nor hearing problems at the time of the study. Participants were otolaryngologists (n = 2), practicing speech language pathologists (n = 13), and a registered nurse working in an Ear Nose and Throat clinic (n = 1).

Voice recordings

Due to social distancing measures during the COVID-19 pandemic, it was impossible for participants to have their voices recorded in the same recording environment. Therefore, voice recordings took place in a room at the practicing clinic of the participants with ambient noise ranging from 33.3 decibels (dBA) to 58.0 dBA. Participants were required to use their habitual voice to read the following standardised tasks: three repetitions of the sustained vowel /a/ for at least 10 s, the Consensus Auditory-Perceptual Evaluation of Voice (CAPE-V) phrases53, and the Rainbow Passage54. These tasks were produced in three conditions with the speaker (1) not wearing a mask, (2) wearing a surgical mask, and (3) wearing a KN95 mask (Fig. 1). The order of conditions was randomised across speakers to minimize biases related to intra-speaker variability in phonation and potential compensation whilst wearing a mask. When wearing these masks, participants were required to use the highest level of fitting to ensure maximal barrier level. They were required to press the nose metal bar so that it fit tightly to the nose contour. The straps of the mask were securely placed behind the auricles and the lower side of the mask was pulled fully downward so that it covered the chin completely (Fig. 1). It has been known that in unfavourable/challenging speaking conditions, speakers may adapt a phonation style that helps improve clear phonation55,56. Therefore, we required participants to maintain similar habitual voice in terms of pitch, loudness, and phonation type throughout recording sessions both with and without a mask to minimise intra-speaker variability in voice production.

All voice signals were captured using an AKG C520 ear-mounted microphone57 placed at a constant distance of 6 cm, 45° off the mouth axis and were analog-to-digital converted using a professional external sound card (Roland Quadcapture58) at 44.1 kHz and 16-bit resolution. The signals were processed and saved to a laptop computer using the Audacity sound editing software in *.wav format59. Calibration of sound level in the voice signals was deemed unnecessary given that the data were used to test within-subject effects of mask and non-mask conditions.

Acoustic analysis

Voice samples were edited in Audacity to extract the middle 3 s of the sustained /a/ vowel, the 3rd CAPE-V phrase (CAPEV-3), and the 2nd and 3rd sentences of the Rainbow Passage (RP23). All acoustic data were measured using Praat version 6.0.3960.

Mean spectral level in low (0–1 kHz) and high (1–8 kHz) frequency ranges

Spectral levels in the 0–1000 Hz and 1000–8000 Hz were measured in Praat for the /a/ vowel (averaged from three repeats) and RP23. 1000 Hz was the cut-off between the low- and high-frequency regions in this study as the spectral region above 1000 Hz has been frequently used in investigating the role of different spectral regions in speech perception25. Consonant noise is mainly concentrated at frequency regions above this frequency61. Further, the 1000 Hz cut-off has been used in studies involving spectral characteristics of voice quality62,63,64. The upper limit of 8 kHz was used as extended high frequency ranges above this frequency have minor value in speech perception65. The protocols in Praat were as follows: From Analyse spectrum =  > To LTAS, set bandwidth = 100 Hz and click OK. From Query =  > Get mean, then frequency bands were set with averaging method being “dB”. The output was then copied to an Excel spreadsheet for analysis.

Low/high spectral energy ratio between 0–1 and 1–8 kHz (LH1000)

We also evaluated the low/high energy ratio (reflecting spectral slope) which is a ratio of spectral energy levels between the low and high frequency ranges to investigate how this would be affected given the impact of mask-wearing on the speech spectrum. The low/high ratio using a 1000 Hz cut-off value (LH1000) has been used frequently in voice and speech research and has been shown to reflect voice quality62,63, vocal load64, sentence prominence in speech66, and the effects of language67.

The low/high energy ratio between spectral areas below 1 Hz and between 1–8 kHz was measured for the /a/ vowel (averaged from three repeats) and RP23 using the long-term average spectra (LTAS) function in Praat. The command to obtain this measure in Praat was as follows: From Analyse spectrum =  > To LTAS, set bandwidth = 100 Hz and click OK. From Query =  > Get slope, set averaging method = dB, low band = 0–1000 Hz, high band = 1000–8000 Hz and click OK. The value that Praat software provided was measured in dB.

Harmonics-to-noise ratio

Praat (version 6.0.39) was also used to measure harmonics-to-noise ratio (HNR) from the sustained /a/ vowel. The 3-s vowel sample was open and highlighted in Praat editing window from which HNR was obtained using the command Voice report within the Pulses menu. Data was averaged from three repeats. Prior to measurement of HNR, all edited vowel samples were signal-typed by the first author (D.D.N.) and a research assistant using criteria recommended by Titze68 and Sprecher et al.69 This was conducted using narrow-band spectrograms generated in Praat using settings described in Sprecher et al.69. Signal typing was performed visually by comparing each spectrogram picture with the exemplar signal types. Signal typing was deemed necessary because the measurement of HNR relies on reliable estimation of F0, which is only feasible in type 1 and type 2 signals69.

Cepstral peak prominence smoothed

The voice cepstrum is obtained by a Fourier transform of the logarithm power spectrum70. A cepstral peak is identified within the dominant ‘rahmonic’ corresponding to the fundamental period from which CPP is calculated as the amplitude between the peak and the regression line directly below it45. Smoothing the individual cepstra before extracting the cepstral peak and calculating CPP can improve prediction accuracy18. CPP-smoothed (CPPS) was measured in Praat using settings as follows71,72: Pitch floor (Hz) = 60, Time steps (s) = 0.002, Maximum frequency (Hz) = 5000, Pre-emphasis from (Hz) = 50, Time averaging window (s) = 0.01, Quefrency averaging window (s) = 0.001, Peak search pitch range (Hz) = 60–330, Tolerance (0–1) = 0.05, Interpolation = Parabolic, Subtract tilt before smoothing = No, Tilt line quefrency range (s) = 0.001–0.0 (= end), Line type = Straight, Fit method = Robust.

Vocal intensity

Vocal intensity (dB) was also measured from the vowel, the 3rd CAPEV phrase, and the 2nd and 3rd sentences of the Rainbow Passage using Praat with default settings. Intensity values were not calibrated to real sound pressure level as the purpose of the study was to examine within-speaker effects.

Quality check of voice recordings and reliability analysis

Because voice recordings took place in different clinic rooms with different levels of background noise, audio files were examined for signal-to-noise ratio (SNR) using a Praat script called Speech-to-noise ratio /Voice-to-noise ratio v.01.0173. Only samples with a SNR greater than 30 dB were used for acoustic analyses74.

The sound files of four participants in all conditions [n = 4 × 3 conditions (no-mask, surgical mask, KN95) = 12], were randomly selected and analysed a second time by a research assistant for HNR and LH1000 to calculate inter-rater reliability using Intraclass Correlation Coefficient (ICC, two-way mixed, consistency type). The results are shown in Table 1, which indicate excellent reliability. ICC was 1.00 for LH1000 as the measurement of this was fully automated using edited voice samples. The slightly lower ICC values for HNR resulted from possible differences between the raters in selecting (highlighting) the vowel segment for HNR measurement in Praat editing window.

Table 1 Inter-rater reliability of acoustic analyses.

Statistical analyses

Data were managed in Microsoft Excel 36575 and analysed using IBM SPSS Statistics v.25.076 and Prism v8.1.277 for Windows. One-way repeated-measures analysis of variance (ANOVA) was used to examine the effects across three conditions (no-mask, surgical mask, and KN95 mask) on acoustic measures. Significant main effects were evaluated with Bonferroni-adjusted tests. Prior to analyses, normal distribution of the data was examined using Kolmogorov–Smirnov tests78. Mauchly’s test of sphericity was performed before ANOVA and, if sphericity assumptions were not met, a Greenhouse–Geisser adjustment was used. Effect size was calculated using partial Eta squared (η2). Effect sizes of 0.01, 0.1, and 0.25 indicated small, medium, and large effects, respectively79. Where normality assumption was not met, the Friedman test was used to compare data across non-mask, surgical mask, and KN95 conditions. A significance level of 0.05 was used.

Results

Mean spectral levels at low and high frequency regions

Mean spectral levels in 0–1 kHz region

Figure 2 showed mean spectral levels at both frequency bands 0–1 kHz and 1–8 kHz. This figure shows that this spectral measure did not change across conditions for both vowel and connected speech. For sustained /a/ vowel phonation, no significant main effects of mask-wearing were found: F(2, 22) = 0.396, p = 0.678, partial η2 = 0.035. For RP23, there was also no significant main effects of masks in the 0–1 kHz range F(1.235, 13.588) = 0.808, p = 0.410, partial η2 = 0.068.

Figure 2
figure 2

Mean spectral levels at low and high frequency regions across conditions. As sound pressure level was not calibrated, spectral levels were normalized so that the lowest value equalled zero. RP23 = The 2nd and 3rd sentences of the Rainbow Passage.

Mean spectral levels in 1–8 kHz region

Figure 2 shows mean spectral levels for both vowel and connected speech in the 1–8 kHz region. For vowel production, no significant main effects of wearing a facemask were observed F(2, 22) = 0.024, p = 0.963, partial η2 = 0.002. However, for RP23, wearing a facemask affected mean spectral levels in the 1–8 kHz region: There was a significant main effect of mask-wearing on this measure F(1.173, 11.735) = 16.951, p = 0.001, partial η2 = 0.629. Post-hoc tests showed that, compared with the non-mask condition, the KN95 mask attenuated the spectral levels in the 1–8 kHz region by 5.2 dB (p = 0.005) while the surgical mask attenuated the spectral levels in this region by 2.0 dB (p = 0.014).

Low/high spectral ratio (LH1000)

LH1000 was calculated for the /a/ vowel and RP23. Figure 3 shows mean and SD of LH1000 for both tasks. One-way repeated-measures ANOVA was calculated to compare data across non-mask, surgical mask, and KN95 mask. For the sustained vowel, no significant main effects were observed: F(2, 22) = 0.949, p = 0.402, partial η2 = 0.079. In Fig. 3, LH1000 for the vowel produced did not change significantly across non-mask and mask conditions.

Figure 3
figure 3

Mean low/high ratio at 1000 Hz for all conditions. Error bars indicate standard deviation. RP23 = The 2nd and 3rd sentences of the Rainbow Passage.

For RP23, a significant main effect was present: F(1.279, 14.073) = 84.346, p = 0.000, partial η2 = 0.885. Figure 3 shows that LH1000 of RP23 was the lowest for the non-mask [mean (SD) = 23.0 (1.7) dB], higher for the surgical mask [mean (SD) = 25.5 (2.2) dB], and highest for the KN95 mask condition [mean (SD) = 28.2 (1.7) dB]. Pairwise Bonferroni-adjusted comparisons showed that wearing a KN95 mask increased the LH1000 of RP23 by 5.2 dB (p = 0.000) and wearing a surgical mask increased the LH1000 of RP by almost 2.5 dB (p = 0.000).

Harmonics-to-noise ratio

Figure 4 shows HNR in all experimental conditions. HNR was compared across non-mask, surgical mask, and KN95 mask conditions using one-way repeated-measures ANOVA; significant main effects were found: F(2, 22) = 14.749, p = 0.000, partial η2 = 0.573. Post-hoc Bonferroni-adjusted tests showed that HNR significantly increased with wearing either a surgical mask or KN95 mask. In the non-mask condition, the mean (SD) of HNR was 25.0 (3.5) dB. HNR values increased significantly to 27.3 (4.5) dB (p = 0.004) and 28.4 (4.1) dB (p = 0.000) when wearing a surgical mask and KN95 mask, respectively.

Figure 4
figure 4

Harmonics-to-noise ratio in all conditions. Error bars = standard deviation.

Cepstral peak prominence smoothed

Figure 5 shows that CPPS did not change across all conditions for all three vocal tasks. One-way repeated-measures ANOVA comparing no-mask, surgical mask, and KN95 mask showed no statistically significant effects of mask-wearing on CPPS of vowel [F(2, 22) = 0.695, p = 0.51, partial η2 = 0.059], CAPEV-3 [F(1.326, 13.260) = 0.013, p = 0.954, partial η2 = 0.001], nor RP23 [F(1.218, 13.393) = 4.313, p = 0.051, partial η2 = 0.282].

Figure 5
figure 5

Cepstral peak prominence smoothed (CPPS) for all conditions. CAPEV-3 = The third CAPE-V phrase. RP23 = The 2nd and 3rd sentences of the Rainbow Passage.

Vocal intensity

The intensity of the vowel increased slightly in both mask conditions however the changes were not statistically significant (Table 2). Similarly, there were no main effects of mask-wearing on vocal intensity of the CAPEV-3 and RP23.

Table 2 Mean (SD) of vocal intensity (dB) in each condition and p values from repeated-measures ANOVA.

Data from all three conditions were used to calculate the Pearson’s correlation coefficient (r) between vocal intensity and CPPS. There were moderate correlations between vocal intensity and CPPS (Vowel: r = 0.595, p = 0.000; CAPEV-3: r = 0.522, p = 0.000; and RP23: r = 0.366, p = 0.014).

Discussion

In this study we hypothesized that voice quality measures (e.g. spectral levels at low- and high-frequency, HNR and CPPS) and audibility measure (vocal intensity) would change during wearing either a surgical mask or KN95 mask. The data confirmed our hypotheses for spectral characteristics, showing a significant decrease in mean spectral levels at high frequency regions (1–8 kHz) and an increase in LH1000 (implying steeper spectral slope) when wearing either a surgical mask or KN95 mask. HNR improved significantly in both mask-wearing conditions. However, CPPS and vocal intensity did not change.

The decreased spectral levels in the 1–8 kHz region was in agreement with a previous modelling experiment. In Goldin et al.’s study14, sound levels between 2 and 7 kHz was attenuated by 3–4 dB with the surgical mask and by approximately 12 dB with the N95 compared with the non-mask condition. In the present study we found that the spectral levels at the 1–8 kHz region was attenuated by 2.0 dB by surgical mask and 5.2 dB by KN95 mask. The findings may be explained as degrading/attenuating effects of mask on spectral levels. These appeared to depend upon the filter performance and the level of fitting of the masks in which the KN95 mask outperforms standard surgical masks. Although both our and Goldin et al.’s studies found greater impact of masks with higher barrier levels (N95/KN95), levels of fitting may have varied in Goldin et al.’s study as they used a model in which face contour and skin characteristics were not similar to human subjects. Perceptual studies have shown that masks with higher filtering characteristics impact more on speech i.e. respirators decreased speech intelligibility scores while surgical masks did not show significant differences in speech intelligibility compared with no-mask condition8. Mendel et al.15 also found that surgical masks did not have a negative effect on speech understanding in both normal hearing and hearing loss groups. Taken together, these data showed that KN95 masks differed from standard surgical masks in level of degradation of the signals, hence listener’s mechanisms of adaptation in speech perception might be different when listening to speech produced whilst wearing these masks. This would imply that surgical masks seem to be a more appropriate choice over the KN95 (similarly to N95 masks in specifications52), given that there is insufficient evidence for selecting N95 masks over surgical masks for protecting HCWs against infectious disease transmission80.

The spectral energy in this study was calculated using data combined from both genders because of the within-subject study design. Between-speaker and between-gender variability in the level of changes in spectral characteristics as a result of the mask was therefore not examined. It is well known that speech spectra carry information of both the larynx (voice source) and the vocal tract (filter)81 and reflect gender characteristics31. Apart from common spectral features across speakers82, there may also be variabilities in characteristics of the speech spectrum across speakers. Between-speaker variability has been observed in source spectral shape and spectral noise in both genders, F3 and F4 and formant dispersion in female voices; and spectral slope in the higher frequencies (from the fourth harmonic to the harmonic closest to 2 kHz and from the harmonic closest to 2 kHz to the harmonic closest to 5 kHz) for male voices82. These are within the 1–8 kHz range investigation in the present study. As such, the impact of mask on phonation may not be the same for all speakers. For these reasons spectral measures should be analysed separately for each gender. However, the limited sample size (12 females and 4 males) did not allow sufficient statistical power for each group. Testing spectral measures that characterize between-speaker variability can help evaluate the impact more specifically for each gender. Speaker-specific and gender-specific source-filter characteristics and phonation and articulation strategy with and without wearing a mask were therefore not examined. It is not known whether male or female voices were affected similarly by wearing a mask.

It is possible that the speakers in this study used an individual strategy to adapt their phonation style, which may also account for the findings. Although the participants were instructed to keep their phonation style constant, it was impossible to control for this. Adaptation in mask-wearing condition may include unconsciously increasing vocal projection to compensate for presence of the mask. Increased HNR has been observed in speakers with increased vocal intensity83. However, in the present study vocal intensity was not significantly different across the three conditions. It was therefore not possible to confirm whether the improved HNR resulted from vocal adaptation. In addition, why HNR was higher in mask-wearing conditions whilst CPPS remained unchanged was unclear. Although HNR and CPPS actually reflect different phenomena in voice quality49, the non-significant changes in CPPS and vocal intensity across conditions did not appear to support the compensation assumption. In addition, phonation compensation in mask wearing may include increased vocal effort, as reported in a previous study13. Increased vocal effort seems associated with a different trend of spectral change. In vocally healthy speakers McKenna and Stepp84 observed that typical phonation style had the highest L/H ratio (a ratio of low to high spectral energy with cut-off at 4 kHz) and this measure decreased steadily from mild to maximal effort (i.e. decreased spectral slope in vocal effort). Meanwhile, the two mask-wearing conditions in the present study showed an opposite trend in spectral slope as expressed in the LH1000 findings.

Regardless of the actual mechanisms, the findings implied that several important high-frequency components might change during wearing these masks. These include the higher formants that are necessary to identify linguistic contents of segmental units. Previous studies have shown that high frequency energy is important to recognize vowels26. As the second formant for the vast majority of English vowels exist above 1 kHz85, it is likely that correct identification of vowels will be compromised if frequencies above 1 kHz are attenuated by the wearing of a facemask. The recognition of voiceless consonants (e.g. /s/ and /f/) also requires detection of high frequency regions above 1 kHz61. For example, the noise component produced in /s/ is centred between 5 and 6 kHz86. The changes in higher frequency regions also imply that the quality of the speech signal will be affected as the frequencies of voiceless plosives, fricatives and affricates occur within 2–4 kHz, as does place of articulation86. Although the findings were not specific to any specific segmental acoustic measures of speech recognition, accurate recognition of speech may be compromised when the speaker is wearing a mask, as demonstrated in some previous studies8,9. It is important to note that the data in this study was recorded in sound-proofed or quiet conditions, while in reality, people are wearing masks in noisy environments, which will most likely further reduce the audibility of the speech signal. The degradation of speech spectral signals, associated with background noise and with acoustically reflective surfaces (e.g. hard uncarpeted floors) will further reduce audibility of the signal and speech perception, making it difficult for listeners, especially those with hearing loss, to perceive and understand the speech signal in such high noise level environments. Further research using landmarks for speech recognition87 would help clarify the specific impact of wearing a mask on important linguistic content e.g. vowels and consonants.

The improved HNR in mask-wearing condition agreed with previous findings that people with voice disorders sound less dysphonic when their mouth is covered. For example, hand-over-mouth has been believed to reduce vocal constriction and increase frontal/oral resonance, facilitating more effective voice production88. Wearing a semi-occluded ventilation mask (SOVM)89 in people with normal voices and functional dysphonia also showed significant differences between SOVM and control in aerodynamic, electroglottographic, and acoustic measures. The improvement in HNR can also be explained using similar mechanisms as for spectral findings i.e. filtering out/attenuation of glottal noise from the signals by the masks. Given that glottal noise components in voice typically appear in high frequency range90,91, noise attenuation by the masks may make spectral energy at low frequency more dominant relative to high frequency, hence improving HNR. In addition, the masks might affect vocal tract resonance, leading to changes in nasal-oral coupling with more nasal resonance involved because of increased impedance at the mouth opening whilst wearing the masks. A previous study has shown that when phonation changed from vowel to nasal, HNR increased by 1.5 dB49.

We did not observe significant changes in vocal intensity for all vocal tasks in mask conditions. This agreed with our non-significant findings on CPPS and was contrary to our expectation that wearing a mask would attenuate intensity. This is possibly due to the calculation of intensity by the Praat program, as default settings of intensity are calculated on frequency settings. In this study, intensity calculations were conducted on frequencies between 75 and 500 Hz. Additionally, although the participants were asked to maintain approximately similar vocal intensity across conditions, they might inadvertently increase their volume slightly in mask conditions to compensate for the altered feedback created by the mask. In addition, the intensity values were obtained from the signals recorded using a cardioid (directional) microphone at distance of 6 cm from the mouth. Whether this applied to perception/audibility of loudness/vocal intensity at real communication distances is not known.

Several issues need consideration in future studies. The main limitation of this study was that it did not evaluate speech intelligibility as the vocal tasks used were standardized for assessing voice quality and not for speech intelligibility. Future studies should compare speech perception across people with normal hearing and hearing impairments using speech materials produced with and without wearing different mask types. This study did not calibrate sound level as its aim was to test within-subject factors; hence, real intensity level was deemed unnecessary. Future studies may measure real sound pressure levels in non-mask and mask conditions to further clarify the impact of mask-wearing on speech sound audibility. Findings presented were for standard surgical mask and KN95 mask only and may not be generalizable to other types of masks. In light of the use of cloth masks amongst members of the community, investigating the effect of fabric masks on both acoustic and auditory-perceptual measures is recommended.

Conclusion

This study showed that the recorded acoustic voice signal changed whilst wearing either a standard surgical mask or a KN95 mask. Low/high spectral ratio increased i.e. spectral slope was steeper, which resulted from an attenuation of mean spectral levels in the 1–8 kHz regions relative to the 0–1 Hz regions. These spectral measures changed to a greater extent for KN95 mask than for surgical mask, suggesting that filtering and fitting characteristics of masks might determine the level of the voice and speech signal degradation. Alternatively, findings may reflect that the degree to which a speaker wearing a mask adjusts their phonation style is different across masks differing in characteristics. The findings appeared to imply that surgical masks might be a more relevant choice over the KN95 in COVID-19 pandemic to minimize the impact on communication.

HNR was improved in both surgical mask and KN95 conditions, implying possible filtering effects of these masks on spectral/glottal noise. This had implications in voice assessment in situations where patients are wearing a mask.

This study did not find significant changes in both CPPS and vocal intensity in the mask condition, implying that the design (e.g. recording setup) of the present study did not allow detection of changes in these measures, or that participants may have altered their vocal production in response to the mask.

References

  1. Institute of Medicine. Reusability of Facemasks During an Influenza Pandemic: Facing the Flu (National Academies Press, 2006).

    Google Scholar 

  2. Gralton, J. & McLaws, M. L. Protecting healthcare workers from pandemic influenza: N95 or surgical masks?. Crit. Care Med. 38, 657–667 (2010).

    PubMed  Article  Google Scholar 

  3. Bartoszko, J. J. et al. Medical masks vs N95 respirators for preventing COVID-19 in healthcare workers: A systematic review and meta-analysis of randomized trials. Influenza Other Respir. Viruses 14, 365–373 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. Leung, N. H. L. et al. Respiratory virus shedding in exhaled breath and efficacy of face masks. Nat. Med. 26, 676–680 (2020).

    CAS  PubMed  Article  Google Scholar 

  5. World Health Organization. Coronavirus Disease (COVID-19) Advice for the Public: When and How to Use Masks https://www.who.int/emergencies/diseases/novel-coronavirus-2019/advice-for-public/when-and-how-to-use-masks (2020).

  6. Johnson, A. T. Respirator masks protect health but impact performance: A review. J. Biol. Eng. 10, 4 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  7. Palmiero, A. J. et al. Speech intelligibility assessment of protective facemasks and air-purifying respirators. J. Occup. Environ. Hyg. 13, 960–968 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. Radonovich, L. J. Jr. et al. Diminished speech intelligibility associated with certain types of respirators worn by healthcare workers. J. Occup. Environ. Hyg. 7, 63–70 (2010).

    PubMed  Article  Google Scholar 

  9. Thomas, F. et al. Does wearing a surgical facemask or N95-respirator impair radio communication?. Air Med. J. 30, 97–102 (2011).

    PubMed  Article  Google Scholar 

  10. Coyne, K. M. et al. Respirator performance ratings for speech intelligibility. Am. Ind. Hyg. Assoc. J. 59, 257–260 (1998).

    CAS  PubMed  Article  Google Scholar 

  11. Atcherson, S. R. et al. The effect of conventional and transparent surgical masks on speech understanding in individuals with and without hearing loss. J. Am. Acad. Audiol. 28, 58–67 (2017).

    PubMed  Article  Google Scholar 

  12. Chodosh, J., Weinstein, B. E. & Blustein, J. Face masks can be devastating for people with hearing loss. BMJ 370, m2683 (2020).

    PubMed  Article  Google Scholar 

  13. Ribeiro, V. V. et al. Effect of wearing a face mask on vocal self-perception during a pandemic. J. Voice https://doi.org/10.1016/j.jvoice.2020.09.006 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Goldin, A., Weinstein, B. & Shiman, N. How do medical masks degrade speech perception?. Hear. Rev. 27, 8–9 (2020).

    Google Scholar 

  15. Mendel, L. L., Gardino, J. A. & Atcherson, S. R. Speech understanding using surgical masks: A problem in health care?. J. Am. Acad. Audiol. 19, 686–695 (2008).

    PubMed  Article  Google Scholar 

  16. Wild, C. J. et al. Effortful listening: The processing of degraded speech depends critically on attention. J. Neurosci. 32, 14010–14021 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Hillenbrand, J. M., Houde, R. A. & Gayvert, R. T. Speech perception based on spectral peaks versus spectral shape. J. Acoust. Soc. Am. 119, 4041–4054 (2006).

    ADS  PubMed  Article  Google Scholar 

  18. Hillenbrand, J. & Houde, R. A. Acoustic correlates of breathy vocal quality: Dysphonic voices and continuous speech. J. Speech Hear. Res. 39, 311–321 (1996).

    CAS  PubMed  Article  Google Scholar 

  19. Healy, E. W. & Bacon, S. P. Measuring the critical band for speech. J. Acoust. Soc. Am. 119, 1083–1091 (2006).

    ADS  PubMed  Article  Google Scholar 

  20. Apoux, F. & Bacon, S. P. Differential contribution of envelope fluctuations across frequency to consonant identification in quiet. J. Acoust. Soc. Am. 123, 2792 (2008).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  21. Dubno, J. R. & Dorman, M. F. Effects of spectral flattening on vowel identification. J. Acoust. Soc. Am. 82, 1503–1511 (1987).

    ADS  CAS  PubMed  Article  Google Scholar 

  22. Sakayori, S. et al. Critical spectral regions for vowel identification. Neurosci. Res. 43, 155–162 (2002).

    PubMed  Article  Google Scholar 

  23. Hughes, G. W. & Halle, M. Spectral properties of fricative consonants. J. Acoust. Soc. Am. 28, 303–310 (1956).

    ADS  Article  Google Scholar 

  24. MacDonald, E. N., Pichora-Fuller, M. K. & Schneider, B. A. Effects on speech intelligibility of temporal jittering and spectral smearing of the high-frequency components of speech. Hear. Res. 261, 63–66 (2010).

    PubMed  Article  Google Scholar 

  25. Hazan, V. & Markham, D. Acoustic-phonetic correlates of talker intelligibility for adults and children. J. Acoust. Soc. Am. 116, 3108–3118 (2004).

    ADS  PubMed  Article  Google Scholar 

  26. Donai, J. J. & Paschall, D. D. Identification of high-pass filtered male, female, and child vowels: The use of high-frequency cues. J. Acoust. Soc. Am. 137, 1971–1982 (2015).

    ADS  PubMed  Article  Google Scholar 

  27. Vitela, A. D., Monson, B. B. & Lotto, A. J. Phoneme categorization relying solely on high-frequency energy. J. Acoust. Soc. Am. 137, EL65–EL70 (2015).

    ADS  PubMed  Article  Google Scholar 

  28. Monson, B. B., Lotto, A. J. & Story, B. H. Gender and vocal production mode discrimination using the high frequencies for speech and singing. Front. Psychol. 5, 1239 (2014).

    PubMed  PubMed Central  Google Scholar 

  29. Macho, D. & Cheng, Y. M. On the use of wideband signal for noise robust ASR. In 2003 IEEE International Conference on Accoustics, Speech, and Signal Processing 109–112.

  30. Boyd-Pratt, H. A. & Donai, J. J. The perception and use of high-frequency speech energy: Clinical and research implications. Perspect. ASHA Spec. Interest Groups 5, 1–9 (2020).

    Article  Google Scholar 

  31. Donai, J. J. & Halbritter, R. M. Gender identification using high-frequency speech energy: Effects of increasing the low-frequency limit. Ear Hear. 38, 65–73 (2017).

    PubMed  Article  Google Scholar 

  32. Maniwa, K., Jongman, A. & Wade, T. Acoustic correlates of intelligibility enhancements in clearly produced fricatives. In 8th Annual Conference of the International Speech Communication Association, Interspeech 2007 1581–1584.

  33. Awan, S. N. et al. Effects of a variably occluded face mask on the aerodynamic and acoustic characteristics of connected speech in patients with and without voice disorders. J. Voice 33(809), e801-809 (2019).

    Google Scholar 

  34. Porcaro, C. K. et al. Effect of dysphonia and cognitive-perceptual listener strategies on speech intelligibility. J. Voice 34(5), 806.e7-806.e18 (2019).

    Article  Google Scholar 

  35. Ishikawa, K. et al. The effect of background noise on intelligibility of dysphonic speech. J. Speech Lang. Hear. Res. 60, 1919–1929 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  36. Qi, Y. & Hillman, R. E. Temporal and spectral estimations of harmonics-to-noise ratio in human voice signals. J. Acoust. Soc. Am. 102, 537–543 (1997).

    ADS  CAS  PubMed  Article  Google Scholar 

  37. Baken, R. J. & Orlikoff, R. F. Clinical Measurement of Speech and Voice 2nd edn. (Singular Thomson Learning, 2000).

    Google Scholar 

  38. Mathew, M. M. & Bhat, J. S. Soft phonation index—A sensitive parameter?. Indian J. Otolaryngol. Head Neck Surg. 61, 127–130 (2009).

    PubMed  PubMed Central  Article  Google Scholar 

  39. Akif Kiliç, M. et al. The effects of vowels on voice perturbation measures. J. Voice 18, 318–324 (2004).

    Article  Google Scholar 

  40. Godino-Llorente, J. I. et al. Acoustic analysis of voice using WPCVox: A comparative study with Multi Dimensional Voice Program. Eur. Arch. Otorhinolaryngol. 265, 465–476 (2008).

    PubMed  Article  Google Scholar 

  41. Heman-Ackah, Y. D., Michael, D. D. & Goding, G. S. Jr. The relationship between cepstral peak prominence and selected parameters of dysphonia. J Voice 16, 20–27 (2002).

    PubMed  Article  Google Scholar 

  42. Warhurst, S. et al. The vocal clarity of female speech-language pathology students: An exploratory study. J. Voice 26, 63–68 (2012).

    PubMed  Article  Google Scholar 

  43. Awan, S. N., Roy, N. & Dromey, C. Estimating dysphonia severity in continuous speech: Application of a multi-parameter spectral/cepstral model. Clin. Linguist. Phon. 23, 825–841 (2009).

    PubMed  Article  Google Scholar 

  44. Maryn, Y. et al. Acoustic measurement of overall voice quality: A meta-analysis. J. Acoust. Soc. Am. 126, 2619–2634 (2009).

    ADS  PubMed  Article  Google Scholar 

  45. Hillenbrand, J., Cleveland, R. A. & Erickson, R. L. Acoustic correlates of breathy vocal quality. J. Speech Hear. Res. 37, 769–778 (1994).

    CAS  PubMed  Article  Google Scholar 

  46. Awan, S. N. & Roy, N. Toward the development of an objective index of dysphonia severity: A four-factor acoustic model. Clin. Linguist. Phon. 20, 35–49 (2006).

    PubMed  Article  Google Scholar 

  47. Awan, S. N., Giovinco, A. & Owens, J. Effects of vocal intensity and vowel type on cepstral analysis of voice. J. Voice 26(670), e615-620 (2012).

    Google Scholar 

  48. Madill, C. et al. Comparison of cepstral peak prominence measures using the ADSV, SpeechTool and VoiceSauce acoustic analysis programs. Acoust. Aust. 46, 215–226 (2018).

    Article  Google Scholar 

  49. Madill, C. et al. The impact of nasalance on Cepstral peak prominence and harmonics-to-noise ratio. Laryngoscope 129, E299–E304 (2019).

    PubMed  Article  Google Scholar 

  50. Phadke, K. V. Selected Topics in Laryngeal, Perceptual and Acoustic Assessments of Human Voice: Videokymographic Evaluations of Vocal Folds and Investigations of Teachers’ Voices. PhD thesis, Palacký University Olomouc (2018).

  51. Australian Government Department of Health. The Use of Face Masks and Respirators in the Context of Covid-19 https://www.health.gov.au/sites/default/files/documents/2020/05/the-use-of-face-masks-and-respirators-in-the-context-of-covid-19.pdf (2020).

  52. 3M. Comparison of FFP2, KN95, and N95 Filtering Facepiece Respirator Classes https://multimedia.3m.com/mws/media/1791500O/comparison-ffp2-kn95-n95-filtering-facepiece-respirator-classes-tb.pdf (2020).

  53. Kempster, G. B. et al. Consensus auditory-perceptual evaluation of voice: Development of a standardized clinical protocol. Am. J. Speech Lang. Pathol. 18, 124–132 (2009).

    PubMed  Article  Google Scholar 

  54. Fairbanks, G. Voice and Articulation DRILLBOOK 2nd edn. (Harper & Row, 1960).

    Google Scholar 

  55. Krause, J. C. & Braida, L. D. Acoustic properties of naturally produced clear speech at normal speaking rates. J. Acoust. Soc. Am. 115, 362–378 (2004).

    ADS  PubMed  Article  Google Scholar 

  56. Hazan, V. et al. Clear speech adaptations in spontaneous speech produced by young and older adults. J. Acoust. Soc. Am. 144, 1331 (2018).

    ADS  PubMed  Article  Google Scholar 

  57. AKG Acoustics. C520 https://www.akg.com/Microphones/Headset%20Microphones/C520.html (2018).

  58. Roland Corp. Quad-Capture—USB 2.0 Audio Interface https://www.roland.com/au/products/quad-capture/ (2019).

  59. Audacity Team. Audacity(R): Free Audio Editor and Recorder [Computer application], https://www.audacityteam.org/ (2019).

  60. Boersma, P. & Weenink, D. Praat: Doing Phonetics by Computer http://www.fon.hum.uva.nl/praat/ (2018).

  61. Strevens, P. Spectra of fricative noise in human speech. Lang. Speech 3, 32–49 (1960).

    Article  Google Scholar 

  62. Frøkjær-Jensen, B. & Prytz, S. Registration of voice quality. Brüel and Kjær – Tech. Rev. 3, 3–17 (1976).

    Google Scholar 

  63. Kitzing, P. LTAS criteria pertinent to the measurement of voice quality. J. Phon. 14, 477–482 (1986).

    Article  Google Scholar 

  64. Rantala, L. et al. Working-day effects on the spectral characteristics of teaching voice. Folia Phoniatr Logop 50, 205–211 (1998).

    CAS  PubMed  Article  Google Scholar 

  65. Monson, B. B. & Caravello, J. The maximum audible low-pass cutoff frequency for speech. J. Acoust. Soc. Am. 146, EL496 (2019).

    ADS  PubMed  Article  Google Scholar 

  66. Kakouros, S., Räsänen, O. & Alku, P. Comparison of spectral tilt measures for sentence prominence in speech—Effects of dimensionality and adverse noise conditions. Speech Commun. 103, 11–26 (2018).

    Article  Google Scholar 

  67. Bahmanbiglu, S. A., Mojiri, F. & Abnavi, F. The Impact Of Language On Voice: An LTAS study. J. Voice 31, 249.e249-249.e212 (2017).

    Article  Google Scholar 

  68. Titze, I. R. Workshop on Acoustic Voice Analysis: Summary Statement (National Center for Voice and Speech, 1995).

    Google Scholar 

  69. Sprecher, A. et al. Updating signal typing in voice: Addition of type 4 signals. J. Acoust. Soc. Am. 127, 3710–3716 (2010).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  70. Noll, A. M. Cepstrum pitch determination. J. Acoust. Soc. Am. 41, 293–309 (1967).

    ADS  CAS  PubMed  Article  Google Scholar 

  71. Watts, C. R., Awan, S. N. & Maryn, Y. A comparison of cepstral peak prominence measures from two acoustic analysis programs. J. Voice 31, 387.e381-387.e310 (2017).

    Article  Google Scholar 

  72. Phadke, K. V. et al. Cepstral and perceptual investigations in female teachers with functionally healthy voice. J. Voice 34, 485.e433-485.e443 (2018).

    Google Scholar 

  73. Maryn, Y. Recording Quality: Speech-to-Noise Ratio and Voice-to-Noise Ratio https://www.phonanium.com/product/recording-quality/ (2020).

  74. Deliyski, D. D., Shaw, H. S. & Evans, M. K. Adverse effects of environmental noise on acoustic voice quality measurements. J. Voice 19, 15–28 (2005).

    PubMed  Article  Google Scholar 

  75. Microsoft. Microsoft Excel https://www.microsoft.com/en-us/microsoft-365/excel (2020).

  76. IBM Corp. IBM SPSS Software https://www.ibm.com/analytics/data-science/predictive-analytics/spss-statistical-software (2018).

  77. GraphPad Software. Prism 8, <https://www.graphpad.com/scientific-software/prism/> (2018).

  78. Massey, F. J. The Kolmogorov–Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46, 68–78 (1951).

    MATH  Article  Google Scholar 

  79. Murphy, K. R. Statistical Power Analysis: A Simple and General Model for Traditional and Modern Hypothesis Tests (Routledge, 2014).

    Book  Google Scholar 

  80. Smith, J. D. et al. Effectiveness of N95 respirators versus surgical masks in protecting health care workers from acute respiratory infection: A systematic review and meta-analysis. CMAJ 188, 567–574 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  81. Fant, G. Glottal source and excitation analysis. Q. Progress Status Rep. 20, 85–107 (1979).

    Google Scholar 

  82. Lee, Y., Keating, P. & Kreiman, J. Acoustic voice variation within and between speakers. J. Acoust. Soc. Am. 146, 1568 (2019).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  83. Sampaio, M. et al. Effects of fundamental frequency, vocal intensity, sample duration, and vowel context in cepstral and spectral measures of dysphonic voices. J. Speech Lang. Hear. Res. 63, 1326–1339 (2020).

    PubMed  Article  Google Scholar 

  84. McKenna, V. S. & Stepp, C. E. The relationship between acoustical and perceptual measures of vocal effort. J. Acoust. Soc. Am. 144, 1643 (2018).

    ADS  PubMed  PubMed Central  Article  Google Scholar 

  85. Kent, R. D. & Vorperian, H. K. Static measurements of vowel formant frequencies and bandwidths: A review. J. Commun. Disord. 74, 74–97 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  86. Ladefoged, P. A Course in Phonetics 6th edn. (Wadsworth, Cengage, 2011).

    Google Scholar 

  87. Chen, F. & Loizou, P. C. Contribution of consonant landmarks to speech recognition in simulated acoustic-electric hearing. Ear Hear. 31, 259–267 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  88. Behrman, A. & Haskell, J. Exercises for Voice Therapy 3rd edn. (Plural Publishing Inc, 2020).

    Google Scholar 

  89. Frisancho, K. et al. Immediate effects of the semi-occluded ventilation mask on subjects diagnosed with functional dysphonia and subjects with normal voices. J. Voice 34, 398–409 (2020).

    PubMed  Article  Google Scholar 

  90. Yanagihara, N. Significance of harmonic changes and noise components in hoarseness. J. Speech Hear. Res. 10, 531–541 (1967).

    CAS  PubMed  Article  Google Scholar 

  91. Shoji, K. et al. High-frequency components of normal voice. J. Voice 5, 29–35 (1991).

    Article  Google Scholar 

Download references

Funding

This study was supported by Doctor Liang Voice Program of The University of Sydney.

Author information

Authors and Affiliations

Authors

Contributions

D.D.N. was involved in research question identification, study planning, protocol preparation, data analysis, data interpretation, manuscript writing and editing, and graphic works. P.M. was involved in study planning, result interpretation, and manuscript reviewing and editing. Donna Thomas was involved in problem identification and manuscript editing. A.P. was involved in problem identification, result interpretation, and manuscript editing. M.D. was involved in problem identification and manuscript reviewing and editing. D.N. interpreted findings and edited the manuscript. A.C. was involved in data collection, data analysis, and manuscript editing. C.M. was involved in problem identification, study planning, protocol preparation, data collection, and manuscript writing and editing. All authors reviewed and approved the manuscript prior submission.

Corresponding author

Correspondence to Catherine Madill.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nguyen, D.D., McCabe, P., Thomas, D. et al. Acoustic voice characteristics with and without wearing a facemask. Sci Rep 11, 5651 (2021). https://doi.org/10.1038/s41598-021-85130-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41598-021-85130-8

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing