Conversational Speech Recognition By Learning Conversation-level
Characteristics
- URL: http://arxiv.org/abs/2202.07855v2
- Date: Thu, 17 Feb 2022 16:24:05 GMT
- Title: Conversational Speech Recognition By Learning Conversation-level
Characteristics
- Authors: Kun Wei, Yike Zhang, Sining Sun, Lei Xie, Long Ma
- Abstract summary: This paper proposes a conversational ASR model which explicitly learns conversation-level characteristics under the prevalent end-to-end neural framework.
Experiments on two Mandarin conversational ASR tasks show that the proposed model achieves a maximum 12% relative character error rate (CER) reduction.
- Score: 25.75615870266786
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Conversational automatic speech recognition (ASR) is a task to recognize
conversational speech including multiple speakers. Unlike sentence-level ASR,
conversational ASR can naturally take advantages from specific characteristics
of conversation, such as role preference and topical coherence. This paper
proposes a conversational ASR model which explicitly learns conversation-level
characteristics under the prevalent end-to-end neural framework. The highlights
of the proposed model are twofold. First, a latent variational module (LVM) is
attached to a conformer-based encoder-decoder ASR backbone to learn role
preference and topical coherence. Second, a topic model is specifically adopted
to bias the outputs of the decoder to words in the predicted topics.
Experiments on two Mandarin conversational ASR tasks show that the proposed
model achieves a maximum 12% relative character error rate (CER) reduction.
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