Analysis of out-of-breath speech for assessment of person's physical fitness. (November 2022)

• Record Type: Journal Article
• Authors: Deb, Suman; Dandapat, Samarendra
• Is Part Of: Computer speech & language. Volume 76(2022)
• Publication Date: 2022-11
• Subjects: Out-of-breath speech -- Physically-active person -- Physically-non-active person -- Fourier model -- Gaussian posteriorgram; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2022.101391
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: In this work, a novel method is proposed for the assessment of a person's physical fitness from out-of-breath speech using Gaussian posteriorgram. Based on physical fitness, we consider two categories of persons, physically-active and physically-non-active. A physically-active person is somebody who regularly does physical exercises like jogging, running, cycling, and playing sports. Out-of-breath speech is recorded from a person immediately after he/she undergoes jogging or physical exercise, and it contains higher breath-emission level than normal speech. It is expected that the breath-emission level will be different for physically-active people than the physically-non-active person in the out-of-breath speech. Due to this, speech characteristics of out-of-breath between physically-active and physically-non-active person categories may differ. To capture this variation, posteriorgram-based features are evaluated on Fourier parameters from out-of-breath speech. Performance is evaluated using recordings of out-of-breath speech from 30 persons. In terms of classification rate, the new feature shows an average classification rate of 91.7% using out-of-breath speech, and it outperforms linear prediction coefficients (LPC), mel frequency cepstral coefficients (MFCC) and non-linear Teager energy operator (TEO) based TEO-CB-Auto-Env features.