Target Speaker Extraction For Overlapped Multi-talker Speaker Verification

Abstract

The performance of speaker verification degrades significantly when the test speech is corrupted by interference speakers. Speaker diarization does well to separate speakers if the speakers are temporally overlapped. However, if multi-talkers speak at the same time, we need the technique to separate the speech in the spectral domain. This paper proposes an overlapped multi-talker speaker verification framework by using target speaker extraction methods. Specifically, given the target speaker information, the target speaker's speech is firstly extracted from the overlapped multi-talker speech by a target speaker extraction module. Then, the extracted speech is passed to the speaker verification system. Experimental results show that the proposed approach significantly improves the performance of overlapped multi-talker speaker verification and achieves 65.7% relative EER reduction.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyrao2019target

Related papers

• Multi-channel Speaker Verification For Single And Multi-talker Speech (2020)0.00
• Audio-visual Active Speaker Extraction For Sparsely Overlapped Multi-talker Speech (2023)7.50
• Multi-class Spectral Clustering With Overlaps For Speaker Diarization (2020)10.35
• Distortionless Multi-channel Target Speech Enhancement For Overlapped Speech Recognition (2020)0.00
• Multi-target Extractor And Detector For Unknown-number Speaker Diarization (2022)8.09
• Cross-channel Attention-based Target Speaker Voice Activity Detection: Experimental Results For M2met Challenge (2022)10.07
• Simultaneous Speech Extraction For Multiple Target Speakers Under The Meeting Scenarios (2022)2.26
• Time-domain Speech Extraction With Spatial Information And Multi Speaker Conditioning Mechanism (2021)7.81