Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis

Amir Hossein Poorjam, Jesper Rindom Jensen, Max A. Little, Mads Græsbøll Christensen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Advances in speech signal analysis facilitate the development of techniques for remote biomedical voice assessment. However, the performance of these techniques is affected by noise and distortion in signals. In this paper, we focus on the vowel /a/ as the most widely-used voice signal for pathological voice assessments and investigate the impact of four major types of distortion that are commonly present during recording or transmission in voice analysis, namely: background noise, reverberation, clipping and compression, on Mel-frequency cepstral coefficients (MFCCs) - the most widely-used features in biomedical voice analysis. Then, we propose a new distortion classification approach to detect the most dominant distortion in such voice signals. The proposed method involves MFCCs as frame-level features and a support vector machine as classifier to detect the presence and type of distortion in frames of a given voice signal. Experimental results obtained from the healthy and Parkinson's voices show the effectiveness of the proposed approach in distortion detection and classification.

Original languageEnglish
Title of host publicationProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017
Pages289-293
Number of pages5
Volume2017-August
DOIs
Publication statusPublished - 24 Aug 2017
Event18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017

Conference

Conference18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017
CountrySweden
CityStockholm
Period20/08/1724/08/17

Bibliographical note

Copyright © 2017 ISCA

Keywords

  • Distortion analysis
  • MFCC
  • Remote biomedical voice assessment
  • Support vector machine

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  • Cite this

    Poorjam, A. H., Jensen, J. R., Little, M. A., & Christensen, M. G. (2017). Dominant distortion classification for pre-processing of vowels in remote biomedical voice analysis. In Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 (Vol. 2017-August, pp. 289-293) https://doi.org/10.21437/Interspeech.2017-378