Leveraging Cross-Utterance Context For ASR Decoding
This work addresses the challenge of improving speech recognition accuracy by leveraging cross-utterance context, offering incremental gains for ASR systems on long-format datasets.
The paper tackled the problem of limited context in automated speech recognition by incorporating long-context transformer language models for cross-utterance decoding via beam search, resulting in a 0.7% and 0.3% absolute reduction in error rates on the AMI dataset compared to single-utterance settings.
While external language models (LMs) are often incorporated into the decoding stage of automated speech recognition systems, these models usually operate with limited context. Cross utterance information has been shown to be beneficial during second pass re-scoring, however this limits the hypothesis space based on the local information available to the first pass LM. In this work, we investigate the incorporation of long-context transformer LMs for cross-utterance decoding of acoustic models via beam search, and compare against results from n-best rescoring. Results demonstrate that beam search allows for an improved use of cross-utterance context. When evaluating on the long-format dataset AMI, results show a 0.7\% and 0.3\% absolute reduction on dev and test sets compared to the single-utterance setting, with improvements when including up to 500 tokens of prior context. Evaluations are also provided for Tedlium-1 with less significant improvements of around 0.1\% absolute.