The impact of face masks on spectral acoustics of speech: Effect of clear and loud speech styles

Knowles, Thea; Badh, Gursharan

doi:10.1121/10.0011400

Cited by 12 publications

(9 citation statements)

References 41 publications

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“…Therefore, a clearer voice and a smaller perturbation (i.e., lower jitter and shimmer) in mask speech production might be a general pattern across languages. However, some scholars have argued that KN95 mask demonstrated a greater effect on speech acoustics than surgical mask [ 13 ]. Therefore, with the missing of other face mask types (e.g., N95 respirator, cloth mask) in the current study, caution must be applied, as the findings might not be applicable to all face masks.…”

Section: Discussionmentioning

confidence: 99%

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The effects of face mask on speech production and its implication for forensic speaker identification-A cross-linguistic study

Geng

Guo

et al. 2023

PLoS ONE

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This study aims to understand the effects of face mask on speech production between Mandarin Chinese and English, and on the automatic classification of mask/no mask speech and individual speakers. A cross-linguistic study on mask speech between Mandarin Chinese and English was then conducted. Continuous speech of the phonetically balanced texts in both Chinese and English versions were recorded from thirty native speakers of Mandarin Chinese (i.e., 15 males and 15 females) with and without wearing a surgical mask. The results of acoustic analyses showed that mask speech exhibited higher F0, intensity, HNR, and lower jitter and shimmer than no mask speech for Mandarin Chinese, whereas higher HNR and lower jitter and shimmer were observed for English mask speech. The results of classification analyses showed that, based on the four supervised learning algorithms (i.e., Linear Discriminant Analysis, Naïve Bayes Classifier, Random Forest, and Support Vector Machine), undesirable performances (i.e., lower than 50%) in classifying the speech with and without a face mask, and highly-variable accuracies (i.e., ranging from 40% to 89.2%) in identifying individual speakers were achieved. These findings imply that the speakers tend to conduct acoustic adjustments to improve their speech intelligibility when wearing surgical mask. However, a cross-linguistic difference in speech strategies to compensate for intelligibility was observed that Mandarin speech was produced with higher F0, intensity, and HNR, while English was produced with higher HNR. Besides, the highly-variable accuracies of speaker identification might suggest that surgical mask would impact the general performance of the accuracy of automatic speaker recognition. In general, therefore, it seems wearing a surgical mask would impact both acoustic-phonetic and automatic speaker recognition approaches to some extent, thus suggesting particular cautions in the real-case practice of forensic speaker identification.

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Section: Discussionmentioning

confidence: 99%

“…Some scholars have also identified the effect of speaking styles on masked speech production. For example, Knowles and Badh have claimed that the overall acoustic patterns of speech wearing face mask are consistent across three speaking styles (i.e., loud speech, clear speech, and habitual speech) [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

The effects of face mask on speech production and its implication for forensic speaker identification-A cross-linguistic study

Geng

Guo

et al. 2023

PLoS ONE

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“…Similarly, Knowles and Badh (2022) measured speech intensity, spectral moments, and spectral tilt and energy of sentences read by 17 healthy talkers with three types of mask-wearing by linear mixed effects regression. With statistical analysis on speech-related and phonation-related variables, Pörschmann et al (2020) analyzed the impact of face masks on voice radiation with audio data generated by a mouth simulator covered with six masks.…”

Section: Audio Datamentioning

confidence: 99%

Human behavior in the time of COVID-19: Learning from big data

et al. 2023

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Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups—using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

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“…Similarly, Knowles and Badh (2022) measured speech intensity, spectral moments, and spectral tilt and energy of sentences read by 17 healthy talkers with three types of maskwearing by linear mixed effects regression. With statistical analysis on speech-related and phonation-related variables, Pörschmann, Lübeck, and Arend (2020) analyzed the impact of face masks on voice radiation with audio data generated by a mouth simulator covered with six masks.…”

Section: Audio Datamentioning

confidence: 99%

Human Behavior in the Time of COVID-19: Learning from Big Data

Lyu¹,

Arsal²,

Zhao³

et al. 2023

Preprint

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Since the World Health Organization (WHO) characterized COVID-19 as a pandemic in March 2020, there have been over 600 million confirmed cases of COVID-19 and more than six million deaths as of October 2022. The relationship between the COVID-19 pandemic and human behavior is complicated. On one hand, human behavior is found to shape the spread of the disease. On the other hand, the pandemic has impacted and even changed human behavior in almost every aspect. To provide a holistic understanding of the complex interplay between human behavior and the COVID-19 pandemic, researchers have been employing big data techniques such as natural language processing, computer vision, audio signal processing, frequent pattern mining, and machine learning. In this study, we present an overview of the existing studies on using big data techniques to study human behavior in the time of the COVID-19 pandemic. In particular, we categorize these studies into three groups -using big data to measure, model, and leverage human behavior, respectively. The related tasks, data, and methods are summarized accordingly. To provide more insights into how to fight the COVID-19 pandemic and future global catastrophes, we further discuss challenges and potential opportunities.

show abstract

The impact of face masks on spectral acoustics of speech: Effect of clear and loud speech styles

Cited by 12 publications

References 41 publications

The effects of face mask on speech production and its implication for forensic speaker identification-A cross-linguistic study

The effects of face mask on speech production and its implication for forensic speaker identification-A cross-linguistic study

Human behavior in the time of COVID-19: Learning from big data

Human Behavior in the Time of COVID-19: Learning from Big Data

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