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Estimating Social Background Profiling of Indian Speakers by Acoustic Speech Features

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Title Estimating Social Background Profiling of Indian Speakers by Acoustic Speech Features
 
Creator Humayun, Mohammad Ali
Yassin, Hayati
Abas, Pg Emeroylariffion
 
Subject Accent identification
Low pass filtering
Ensemble learning
Native language identification
Speaker profiling
 
Description 851-860
Social background profiling of speakers refers to estimating the geographical origin of speakers by their speech features. Methods for accent profiling that use linguistic features, require phoneme alignment and transcription of the speech samples. This paper proposes a purely acoustic accent profiling model, composed of multiple convolutional networks with global average-pooling layers, to classify the temporal sequence of acoustic features. The bottleneck representations of the convolutional networks, trained with the original signals and their low-pass filtered copies, are fed to a Support Vector Machine classifier for final prediction. The model has been analysed for a speech dataset of Indian speakers from social backgrounds spread across India. It has been shown that up to 85% accuracy is achievable for classifying the geographic origin of speakers corresponding to regional Indian languages; 17% higher than the benchmark deep learning model using the same features. Results have also indicated that classification of accents is easier using the second language of the speakers, as compared to their native language.
 
Date 2023-08-09T04:25:15Z
2023-08-09T04:25:15Z
2023-08
 
Type Article
 
Identifier 0022-4456 (Print); 0975-1084 (Online)
http://nopr.niscpr.res.in/handle/123456789/62411
https://doi.org/10.56042/jsir.v82i08.3122
 
Language en
 
Publisher NIScPR-CSIR,India
 
Source JSIR Vol.82(08) [August 2023]