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Discriminative feature domains for reverberant acoustic environments

Discriminative feature domains for reverberant acoustic environments
Discriminative feature domains for reverberant acoustic environments

Several speech processing and audio data-mining applications rely on a description of the acoustic environment as a feature vector for classification. The discriminative properties of the feature domain play a crucial role in the effectiveness of these methods. In this work, we consider three environment identification tasks and the task of acoustic model selection for speech recognition. A set of acoustic parameters and Machine Learning algorithms for feature selection are used and an analysis is performed on the resulting feature domains for each task. In our experiments, a classification accuracy of 100% is achieved for the majority of tasks and the Word Error Rate is reduced by 20.73 percentage points for Automatic Speech Recognition when using the resulting domains. Experimental results indicate a significant dissimilarity in the parameter choices for the composition of the domains, which highlights the importance of the feature selection process for individual applications.

Environment Identification, Feature Selection, Machine Learning, Reverberant speech recognition
1520-6149
756-760
Institute of Electrical and Electronics Engineers Inc.
Papayiannis, Constantinos
eb7beecd-5217-4171-8c45-ce853dbd03f5
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b
Papayiannis, Constantinos
eb7beecd-5217-4171-8c45-ce853dbd03f5
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b

Papayiannis, Constantinos, Evers, Christine and Naylor, Patrick A. (2017) Discriminative feature domains for reverberant acoustic environments. In 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. pp. 756-760 . (doi:10.1109/ICASSP.2017.7952257).

Record type: Conference or Workshop Item (Paper)

Abstract

Several speech processing and audio data-mining applications rely on a description of the acoustic environment as a feature vector for classification. The discriminative properties of the feature domain play a crucial role in the effectiveness of these methods. In this work, we consider three environment identification tasks and the task of acoustic model selection for speech recognition. A set of acoustic parameters and Machine Learning algorithms for feature selection are used and an analysis is performed on the resulting feature domains for each task. In our experiments, a classification accuracy of 100% is achieved for the majority of tasks and the Word Error Rate is reduced by 20.73 percentage points for Automatic Speech Recognition when using the resulting domains. Experimental results indicate a significant dissimilarity in the parameter choices for the composition of the domains, which highlights the importance of the feature selection process for individual applications.

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More information

Published date: 16 June 2017
Venue - Dates: 2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017, , New Orleans, United States, 2017-03-05 - 2017-03-09
Keywords: Environment Identification, Feature Selection, Machine Learning, Reverberant speech recognition

Identifiers

Local EPrints ID: 444978
URI: http://eprints.soton.ac.uk/id/eprint/444978
ISSN: 1520-6149
PURE UUID: fcd28147-a244-4dd9-9817-c3743d4c3142
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

Catalogue record

Date deposited: 13 Nov 2020 17:34
Last modified: 18 Feb 2021 17:41

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Contributors

Author: Constantinos Papayiannis
Author: Christine Evers ORCID iD
Author: Patrick A. Naylor

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