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Speech Acoustics

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Computing nasalance with MFCCs and Convolutional Neural Networks.

PloS one
Nasalance is a valuable clinical biomarker for hypernasality. It is computed as the ratio of acoustic energy emitted through the nose to the total energy emitted through the mouth and nose (eNasalance). A new approach is proposed to compute nasalance...

ADT Network: A Novel Nonlinear Method for Decoding Speech Envelopes From EEG Signals.

Trends in hearing
Decoding speech envelopes from electroencephalogram (EEG) signals holds potential as a research tool for objectively assessing auditory processing, which could contribute to future developments in hearing loss diagnosis. However, current methods stru...

Research on Tone Enhancement of Mandarin Pitch Controllable Electrolaryngeal Speech Based on Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
The deep learning-based electrolaryngeal (EL) voice conversion methods have achieved good results in non-tonal languages. However, the effectiveness in tonal languages, such as Mandarin Chinese (Mandarin), remains suboptimal. The reason may be that t...

The Mason-Alberta Phonetic Segmenter: a forced alignment system based on deep neural networks and interpolation.

Phonetica
Given an orthographic transcription, forced alignment systems automatically determine boundaries between segments in speech, facilitating the use of large corpora. In the present paper, we introduce a neural network-based forced alignment system, the...

Using articulatory feature detectors in progressive networks for multilingual low-resource phone recognitiona).

The Journal of the Acoustical Society of America
Systems inspired by progressive neural networks, transferring information from end-to-end articulatory feature detectors to similarly structured phone recognizers, are described. These networks, connecting the corresponding recurrent layers of pre-tr...

The voice of depression: speech features as biomarkers for major depressive disorder.

BMC psychiatry
BACKGROUND: Psychiatry faces a challenge due to the lack of objective biomarkers, as current assessments are based on subjective evaluations. Automated speech analysis shows promise in detecting symptom severity in depressed patients. This project ai...

Automated segmentation of child-clinician speech in naturalistic clinical contexts.

Research in developmental disabilities
BACKGROUND: Computational approaches hold significant promise for enhancing diagnosis and therapy in child and adolescent clinical practice. Clinical procedures heavily depend n vocal exchanges and interpersonal dynamics conveyed through speech. Rese...

The machine learning-based prediction of the sound pressure level from pathological and healthy speech signals.

The Journal of the Acoustical Society of America
Vocal intensity is quantified by sound pressure level (SPL). The SPL can be measured by either using a sound level meter or by comparing the energy of the recorded speech signal with the energy of the recorded calibration tone of a known SPL. Neither...

A WaveNet-based model for predicting the electroglottographic signal from the acoustic voice signal.

The Journal of the Acoustical Society of America
The electroglottographic (EGG) signal offers a non-invasive approach to analyze phonation. It is known, if not obvious, that the onset of vocal fold contacting has a substantial effect on how the vocal folds vibrate and on the quality of the voice. G...