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Discrimination between Modal, Breathy and Pressed Voice for Single Vowels Using Neck-Surface Vibration Signals

1
Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
2
School of Communication Sciences and Disorders, McGill University, Montreal, QC H3A 0G4, Canada
*
Author to whom correspondence should be addressed.
Current address: 845 Sherbrooke Street West, Montreal, QC H3A 0G4, Canada.
Appl. Sci. 2019, 9(7), 1505; https://doi.org/10.3390/app9071505
Received: 8 March 2019 / Revised: 4 April 2019 / Accepted: 6 April 2019 / Published: 11 April 2019
(This article belongs to the Special Issue Computational Methods and Engineering Solutions to Voice)
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Abstract

The purpose of this study was to investigate the feasibility of using neck-surface acceleration signals to discriminate between modal, breathy and pressed voice. Voice data for five English single vowels were collected from 31 female native Canadian English speakers using a portable Neck Surface Accelerometer (NSA) and a condenser microphone. Firstly, auditory-perceptual ratings were conducted by five clinically-certificated Speech Language Pathologists (SLPs) to categorize voice type using the audio recordings. Intra- and inter-rater analyses were used to determine the SLPs’ reliability for the perceptual categorization task. Mixed-type samples were screened out, and congruent samples were kept for the subsequent classification task. Secondly, features such as spectral harmonics, jitter, shimmer and spectral entropy were extracted from the NSA data. Supervised learning algorithms were used to map feature vectors to voice type categories. A feature wrapper strategy was used to evaluate the contribution of each feature or feature combinations to the classification between different voice types. The results showed that the highest classification accuracy on a full set was 82.5%. The breathy voice classification accuracy was notably greater (approximately 12%) than those of the other two voice types. Shimmer and spectral entropy were the best correlated metrics for the classification accuracy. View Full-Text
Keywords: neck-surface vibration; machine learning; voice type discrimination neck-surface vibration; machine learning; voice type discrimination
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Lei, Z.; Kennedy, E.; Fasanella, L.; Li-Jessen, N. .-K.; Mongeau, L. Discrimination between Modal, Breathy and Pressed Voice for Single Vowels Using Neck-Surface Vibration Signals. Appl. Sci. 2019, 9, 1505.

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