Contextual Density Ratio for Language Model Biasing of Sequence to Sequence ASR Systems

Abstract: End-2-end (E2E) models have become increasingly popular in some ASR tasks because of their performance and advantages. These E2E models directly approximate the posterior distribution of tokens given the acoustic inputs. Consequently, the E2E systems implicitly define a LLM (LM) over the output tokens, which makes the exploitation of independently trained LLMs less straightforward than in conventional ASR systems. This makes it difficult to dynamically adapt E2E ASR system to contextual profiles for better recognizing special words such as named entities. In this work, we propose a contextual density ratio approach for both training a contextual aware E2E model and adapting the LLM to named entities. We apply the aforementioned technique to an E2E ASR system, which transcribes doctor and patient conversations, for better adapting the E2E system to the names in the conversations. Our proposed technique achieves a relative improvement of up to 46.5% on the names over an E2E baseline without degrading the overall recognition accuracy of the whole test set. Moreover, it also surpasses a contextual shallow fusion baseline by 22.1 % relative.