Cross-Utterance Language Models with Acoustic Error Sampling

19 Aug 2020 G. Sun C. Zhang P. C. Woodland

The effective exploitation of richer contextual information in language models (LMs) is a long-standing research problem for automatic speech recognition (ASR). A cross-utterance LM (CULM) is proposed in this paper, which augments the input to a standard long short-term memory (LSTM) LM with a context vector derived from past and future utterances using an extraction network... (read more)

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