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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceeding

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.
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Details

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 publicationINTERSPEECH
Number of pages5
Publication dateAug 2017
Pages289-293
StatePublished - Aug 2017
Publication categoryResearch
Peer-reviewedYes
EventInterspeech 2017 - Stockholm, Sweden
Duration: 20 Aug 201724 Aug 2017
http://www.interspeech2017.org/

Conference

ConferenceInterspeech 2017
LandSweden
ByStockholm
Periode20/08/201724/08/2017
Internetadresse

    Research areas

  • distortion analysis, MFCC, remote biomedical voice assessment, Support vector machine (SVM)

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ID: 263832592