Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakers

• URL: http://arxiv.org/abs/2010.11803v2
• Date: Wed, 10 Feb 2021 15:47:18 GMT
• Title: Compositional embedding models for speaker identification and diarization with simultaneous speech from 2+ speakers
• Authors: Zeqian Li, Jacob Whitehill
• Abstract summary: We propose a new method for speaker diarization that can handle overlapping speech with 2+ people. Our method is based on compositional embeddings.
• Score: 25.280566939206714
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a new method for speaker diarization that can handle overlapping speech with 2+ people. Our method is based on compositional embeddings [1]: Like standard speaker embedding methods such as x-vector [2], compositional embedding models contain a function f that separates speech from different speakers. In addition, they include a composition function g to compute set-union operations in the embedding space so as to infer the set of speakers within the input audio. In an experiment on multi-person speaker identification using synthesized LibriSpeech data, the proposed method outperforms traditional embedding methods that are only trained to separate single speakers (not speaker sets). In a speaker diarization experiment on the AMI Headset Mix corpus, we achieve state-of-the-art accuracy (DER=22.93%), slightly higher than the previous best result (23.82% from [3]).

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