Conditioning a hybrid CTC/attention ASR on speaker embeddings and adding transfer learning from clean speech reduces word error rate on overlapped two-speaker speech to 14.6%, from a prior best of 25.4%.
End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning
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abstract
This paper presents our latest investigation on end-to-end automatic speech recognition (ASR) for overlapped speech. We propose to train an end-to-end system conditioned on speaker embeddings and further improved by transfer learning from clean speech. This proposed framework does not require any parallel non-overlapped speech materials and is independent of the number of speakers. Our experimental results on overlapped speech datasets show that joint conditioning on speaker embeddings and transfer learning significantly improves the ASR performance.
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End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning
Conditioning a hybrid CTC/attention ASR on speaker embeddings and adding transfer learning from clean speech reduces word error rate on overlapped two-speaker speech to 14.6%, from a prior best of 25.4%.