End-to-End Speaker-Attributed ASR with Transformer

Naoyuki Kanda; Guoli Ye; Yashesh Gaur; Xiaofei Wang; Zhong Meng; Zhuo Chen; Takuya Yoshioka

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arXiv · 2104.02128

End-to-End Speaker-Attributed ASR with Transformer

Naoyuki Kanda·Guoli Ye·Yashesh Gaur·Xiaofei Wang·Zhong Meng·Zhuo Chen·Takuya Yoshioka

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Abstract

This paper presents our recent effort on end-to-end speaker-attributed automatic speech recognition, which jointly performs speaker counting, speech recognition and speaker identification for monaural multi-talker audio. Firstly, we thoroughly update the model architecture that was previously designed based on a long short-term memory (LSTM)-based attention encoder decoder by applying transformer architectures. Secondly, we propose a speaker deduplication mechanism to reduce speaker identification errors in highly overlapped regions. Experimental results on the LibriSpeechMix dataset shows that the transformer-based architecture is especially good at counting the speakers and that the proposed model reduces the speaker-attributed word error rate by 47% over the LSTM-based baseline. Furthermore, for the LibriCSS dataset, which consists of real recordings of overlapped speech, the proposed model achieves concatenated minimum-permutation word error rates of 11.9% and 16.3% with and without target speaker profiles, respectively, both of which are the state-of-the-art results for LibriCSS with the monaural setting.

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BibTeX RIS

Naoyuki Kanda, Guoli Ye, Yashesh Gaur, Xiaofei Wang, Zhong Meng, Zhuo Chen, Takuya Yoshioka. 2021-04-05. End-to-End Speaker-Attributed ASR with Transformer. https://arxiv.org/abs/2104.02128

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