Projects per year
Abstract
Voice activity detection (VAD) algorithms are essential for many speech processing applications, such as speaker diarization, automatic speech recognition, speech enhancement, and speech coding. With a good VAD algorithm, non-speech segments can be excluded to improve the performance and computation of these applications. In this paper, we propose a polynomial eigenvalue decomposition-based target-speaker VAD algorithm to detect unseen target speakers in the presence of competing talkers. The proposed approach uses frame-based processing across multi-microphones to compute the syndrome energy, used for testing the presence or absence of a target speaker. The proposed approach is consistently among the best in F1 and balanced accuracy scores over the investigated range of signal to interference ratio (SIR) from -10 dB to 20 dB.
Original language | English |
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Pages | 1-5 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 17 Oct 2022 |
Event | 17th International Workshop on Acoustic Signal Enhancement - Bamberg, Germany Duration: 5 Sept 2022 → 8 Sept 2022 |
Conference
Conference | 17th International Workshop on Acoustic Signal Enhancement |
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Abbreviated title | IWAENC 2022 |
Country/Territory | Germany |
City | Bamberg |
Period | 5/09/22 → 8/09/22 |
Keywords
- polynomial eigenvalue decomposition
- target speaker voice activity detection
- speaker activity detection
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- 1 Finished
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Signal Processing in the Information Age (UDRC III)
Weiss, S. (Principal Investigator) & Stankovic, V. (Co-investigator)
EPSRC (Engineering and Physical Sciences Research Council)
1/07/18 → 31/03/24
Project: Research