Topic identification techniques applied to dynamic language model adaptation for automatic speech recognition

Highlights：

• We present an approach for the dynamic adaptation of the LMs used by a speech recognizer based on topic identification.

• For each audio segment the system interpolates a static LM with a topic-based LM in a two stages recognition architecture.

• The interpolation weights are computed based on different sources of information (distance of LMs and topic similarity).

• We evaluate different strategies for generating topic-specific LMs and for the adaptation of the LM used in the final stage.

• Evaluation shows a significant reduction of the error rates for both tasks (topic identification and speech recognition).