An Empirical Study of Language Model Integration for Transducer based Speech Recognition
This work addresses a specific bottleneck in speech recognition for researchers and practitioners, offering an incremental improvement over prior integration techniques.
The paper tackled the challenge of integrating external language models into RNN-Transducer speech recognition by proposing a low-order density ratio method, which consistently outperformed shallow fusion and performed competitively with or better than existing methods like density ratio and internal language model estimation across English and Chinese datasets.
Utilizing text-only data with an external language model (ELM) in end-to-end RNN-Transducer (RNN-T) for speech recognition is challenging. Recently, a class of methods such as density ratio (DR) and internal language model estimation (ILME) have been developed, outperforming the classic shallow fusion (SF) method. The basic idea behind these methods is that RNN-T posterior should first subtract the implicitly learned internal language model (ILM) prior, in order to integrate the ELM. While recent studies suggest that RNN-T only learns some low-order language model information, the DR method uses a well-trained neural language model with full context, which may be inappropriate for the estimation of ILM and deteriorate the integration performance. Based on the DR method, we propose a low-order density ratio method (LODR) by replacing the estimation with a low-order weak language model. Extensive empirical experiments are conducted on both in-domain and cross-domain scenarios on English LibriSpeech & Tedlium-2 and Chinese WenetSpeech & AISHELL-1 datasets. It is shown that LODR consistently outperforms SF in all tasks, while performing generally close to ILME and better than DR in most tests.