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Multiple hypothesis tracking for overlapping speaker segmentation

Multiple hypothesis tracking for overlapping speaker segmentation
Multiple hypothesis tracking for overlapping speaker segmentation
Speaker segmentation is an essential part of any diarization system. Applications of diarization include tasks such as speaker indexing, improving automatic speech recognition (ASR) performance and making single speaker-based algorithms available for use in multi-speaker environments. This paper proposes a multiple hypothesis tracking (MHT) method that exploits the harmonic structure associated with the pitch in voiced speech in order to segment the onsets and end-points of speech from multiple, overlapping speakers. The proposed method is evaluated against a segmentation system from the literature that uses a spectral representation and is based on employing bidirectional long short term memory networks (BLSTM). The proposed method is shown to achieve comparable performance for segmenting overlapping speakers only using the pitch harmonic information in the MHT framework.
IEEE
Hogg, Aidan
e2c97ca1-9ec2-4da1-9fd3-5feea6142756
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b
Hogg, Aidan
e2c97ca1-9ec2-4da1-9fd3-5feea6142756
Evers, Christine
93090c84-e984-4cc3-9363-fbf3f3639c4b
Naylor, Patrick A.
13079486-664a-414c-a1a2-01a30bf0997b

Hogg, Aidan, Evers, Christine and Naylor, Patrick A. (2019) Multiple hypothesis tracking for overlapping speaker segmentation. In Proceedings IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE.. (doi:10.1109/WASPAA.2019.8937185).

Record type: Conference or Workshop Item (Paper)

Abstract

Speaker segmentation is an essential part of any diarization system. Applications of diarization include tasks such as speaker indexing, improving automatic speech recognition (ASR) performance and making single speaker-based algorithms available for use in multi-speaker environments. This paper proposes a multiple hypothesis tracking (MHT) method that exploits the harmonic structure associated with the pitch in voiced speech in order to segment the onsets and end-points of speech from multiple, overlapping speakers. The proposed method is evaluated against a segmentation system from the literature that uses a spectral representation and is based on employing bidirectional long short term memory networks (BLSTM). The proposed method is shown to achieve comparable performance for segmenting overlapping speakers only using the pitch harmonic information in the MHT framework.

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Published date: 23 December 2019

Identifiers

Local EPrints ID: 439390
URI: http://eprints.soton.ac.uk/id/eprint/439390
PURE UUID: 8ec1af6a-07f9-4e73-9f6f-6aa7a45ebcf2
ORCID for Christine Evers: ORCID iD orcid.org/0000-0003-0757-5504

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Date deposited: 21 Apr 2020 16:30
Last modified: 23 May 2020 00:47

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Contributors

Author: Aidan Hogg
Author: Christine Evers ORCID iD
Author: Patrick A. Naylor

University divisions

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