The Effect of Clear Speech on Cantonese Alaryngeal Speakers’ Intelligibility

Hui, Tak Fai; Cox, Steven R.; Huang, Ting; Chen, Weirong; Ng, Manwa L.

doi:10.1159/000517676

Cited by 3 publications

(1 citation statement)

References 55 publications

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“…Evaluators may also have biases or preconceived notions about alaryngeal speech, which can affect their judgment [27]. Second, alaryngeal speech can be complex and variable, depending on the individual's chosen method of alaryngeal speech and their level of proficiency [28], among other factors. Evaluators may need to consider multiple aspects of speech, such as pitch, tone, articulation, and prosody, which can make the evaluation more challenging [29].…”

Section: Assessing Speech-signal Impairmentsmentioning

confidence: 99%

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Maskeliūnas,

Damaševičius,

Kulikajevas

et al. 2023

Cancers

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The problem of cleaning impaired speech is crucial for various applications such as speech recognition, telecommunication, and assistive technologies. In this paper, we propose a novel approach that combines Pareto-optimized deep learning with non-negative matrix factorization (NMF) to effectively reduce noise in impaired speech signals while preserving the quality of the desired speech. Our method begins by calculating the spectrogram of a noisy voice clip and extracting frequency statistics. A threshold is then determined based on the desired noise sensitivity, and a noise-to-signal mask is computed. This mask is smoothed to avoid abrupt transitions in noise levels, and the modified spectrogram is obtained by applying the smoothed mask to the signal spectrogram. We then employ a Pareto-optimized NMF to decompose the modified spectrogram into basis functions and corresponding weights, which are used to reconstruct the clean speech spectrogram. The final noise-reduced waveform is obtained by inverting the clean speech spectrogram. Our proposed method achieves a balance between various objectives, such as noise suppression, speech quality preservation, and computational efficiency, by leveraging Pareto optimization in the deep learning model. The experimental results demonstrate the effectiveness of our approach in cleaning alaryngeal speech signals, making it a promising solution for various real-world applications.

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Section: Assessing Speech-signal Impairmentsmentioning

confidence: 99%

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Maskeliūnas,

Damaševičius,

Kulikajevas

et al. 2023

Cancers

View full text Add to dashboard Cite

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An acoustic study of Cantonese alaryngeal speech in different speaking conditions

Cox

Huang

Chen

et al. 2023

The Journal of the Acoustical Society of America

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Esophageal (ES) speech, tracheoesophageal (TE) speech, and the electrolarynx (EL) are common methods of communication following the removal of the larynx. Our recent study demonstrated that intelligibility may increase for Cantonese alaryngeal speakers using clear speech (CS) compared to their everyday “habitual speech” (HS), but the reasoning is still unclear [Hui, Cox, Huang, Chen, and Ng (2022). Folia Phoniatr. Logop. 74, 103–111]. The purpose of this study was to assess the acoustic characteristics of vowels and tones produced by Cantonese alaryngeal speakers using HS and CS. Thirty-one alaryngeal speakers (9 EL, 10 ES, and 12 TE speakers) read The North Wind and the Sun passage in HS and CS. Vowel formants, vowel space area (VSA), speaking rate, pitch, and intensity were examined, and their relationship to intelligibility were evaluated. Statistical models suggest that larger VSAs significantly improved intelligibility, but slower speaking rate did not. Vowel and tonal contrasts did not differ between HS and CS for all three groups, but the amount of information encoded in fundamental frequency and intensity differences between high and low tones positively correlated with intelligibility for TE and ES groups, respectively. Continued research is needed to understand the effects of different speaking conditions toward improving acoustic and perceptual characteristics of Cantonese alaryngeal speech.

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Assessment of Self-Supervised Denoising Methods for Esophageal Speech Enhancement

Amarjouf,

Ibn Elhaj,

Chami

et al. 2024

Applied Sciences

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Esophageal speech (ES) is a pathological voice that is often difficult to understand. Moreover, acquiring recordings of a patient’s voice before a laryngectomy proves challenging, thereby complicating enhancing this kind of voice. That is why most supervised methods used to enhance ES are based on voice conversion, which uses healthy speaker targets, things that may not preserve the speaker’s identity. Otherwise, unsupervised methods for ES are mostly based on traditional filters, which cannot alone beat this kind of noise, making the denoising process difficult. Also, these methods are known for producing musical artifacts. To address these issues, a self-supervised method based on the Only-Noisy-Training (ONT) model was applied, consisting of denoising a signal without needing a clean target. Four experiments were conducted using Deep Complex UNET (DCUNET) and Deep Complex UNET with Complex Two-Stage Transformer Module (DCUNET-cTSTM) for assessment. Both of these models are based on the ONT approach. Also, for comparison purposes and to calculate the evaluation metrics, the pre-trained VoiceFixer model was used to restore the clean wave files of esophageal speech. Even with the fact that ONT-based methods work better with noisy wave files, the results have proven that ES can be denoised without the need for clean targets, and hence, the speaker’s identity is retained.

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The Effect of Clear Speech on Cantonese Alaryngeal Speakers’ Intelligibility

Cited by 3 publications

References 55 publications

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

Pareto-Optimized Non-Negative Matrix Factorization Approach to the Cleaning of Alaryngeal Speech Signals

An acoustic study of Cantonese alaryngeal speech in different speaking conditions

Assessment of Self-Supervised Denoising Methods for Esophageal Speech Enhancement

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