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Target-Speaker Voice Activity Detection Via Sequence-to-Sequence Prediction

Year: 2023

Type: conference-paper

Abstract: Target-speaker voice activity detection is currently a promising approach for speaker diarization in complex acoustic environments. This paper presents a novel Sequence-to-Sequence Target-Speaker Voice Activity Detection (Seq2Seq-TSVAD) method that can efficiently address the joint modeling of large-scale speakers and predict high-resolution voice activities. Experimental results show that larger speaker capacity and higher output resolution can significantly reduce the diarization error rate (DER), which achieves the new state-of-the-art performance of 4.55% on the VoxConverse test set and 10.77% on Track 1 of the DIHARD-III evaluation set under the widely-used evaluation metrics.

Source: IEEE International Conference on Acoustics Speech and Signal Processing

DOI: 10.1109/icassp49357.2023.10094752

Authors: Ming Cheng, Weiqing Wang, Yucong Zhang, Xiaoyi Qin, Ming Li

Institutions: Wuhan University, Duke Kunshan University

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Science and Technology Program of Suzhou

OpenAlex ID: W4375868885

FWCI: 4.529

Cites: 34

Cited by: 41

Related to: 10