Evaluating noise suppression methods for recovering the Lombard speech from vocal output in an external noise field

Speech production while wearing hearing protectors poses significant challenges due to their occlusion effect and disruption of the Lombard effect. An experiment was conducted with 24 individuals as they read a list of 12 sentences in open ears and while wearing an earmuff in quiet and in four different noises [pink, International Female Fluctuating Masker (IFFM), speech-spectrum noise (SSnoise), and helicopter] at two levels (70 and 85 dBA). An acoustic manikin, fitted or not with an identical protector, served as the target listener. In noise, speech levels decreased when the talkers wore the earmuff but increased when the target listener was fitted with the earmuff. When the earmuff was used by both the talkers and target listener, speech levels were lower by 3–6 dB at the higher noise level compared to when they were both open ears. Speech levels were typically lower, but extended speech intelligibility index estimates were consistently higher, in fluctuating (IFFM, helicopter) than in continuous noises (pink, SSnoise). Talkers' pitch frequency and voice spectrum measurements followed very closely the changes in speech levels, showing no evidence of compensatory voice modifications. Implications of the lower talker speech levels when wearing hearing protectors are discussed in terms of protector selection, training, and individuals with hearing loss.

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Lightweight Deep Learning Model for Assessment of Substitution Voicing and Speech after Laryngeal Carcinoma Surgery

Maskeliūnas

Kulikajevas

Damaševičius

et al. 2022

Cancers

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Laryngeal carcinoma is the most common malignant tumor of the upper respiratory tract. Total laryngectomy provides complete and permanent detachment of the upper and lower airways that causes the loss of voice, leading to a patient’s inability to verbally communicate in the postoperative period. This paper aims to exploit modern areas of deep learning research to objectively classify, extract and measure the substitution voicing after laryngeal oncosurgery from the audio signal. We propose using well-known convolutional neural networks (CNNs) applied for image classification for the analysis of voice audio signal. Our approach takes an input of Mel-frequency spectrogram (MFCC) as an input of deep neural network architecture. A database of digital speech recordings of 367 male subjects (279 normal speech samples and 88 pathological speech samples) was used. Our approach has shown the best true-positive rate of any of the compared state-of-the-art approaches, achieving an overall accuracy of 89.47%.

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Evaluating noise suppression methods for recovering the Lombard speech from vocal output in an external noise field

Cited by 3 publications

References 34 publications

A Comprehensive Investigation into The Noise Reduction Techniques for Speech

A Comprehensive Investigation into The Noise Reduction Techniques for Speech

The effect of hearing protection worn by talker and/or target listener on speech production in quiet and noise

Lightweight Deep Learning Model for Assessment of Substitution Voicing and Speech after Laryngeal Carcinoma Surgery

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