On Language Model Integration for RNN Transducer based Speech Recognition

Understand

The mismatch between an external language model (LM) and the implicitly learned internal LM (ILM) of RNN-Transducer (RNN-T) can limit the performance of LM integration such as simple shallow fusion.

• A Bayesian interpretation suggests to remove this sequence prior as ILM correction.
• In this work, we study various ILM correction-based LM integration methods formulated in a common RNN-T framework.
• We provide a decoding interpretation on two major reasons for performance improvement with ILM correction, which is further experimentally verified with detailed analysis.