Bridging the gap between streaming and non-streaming ASR systems bydistilling ensembles of CTC and RNN-T models
This work addresses the problem of high word error rates in streaming ASR systems for real-time applications, representing an incremental improvement over existing distillation methods.
The paper tackles the performance gap between streaming and non-streaming ASR systems by distilling ensembles of CTC and RNN-T teacher models, resulting in WER reductions of 41% on Spanish, 27% on Portuguese, and 13% on French compared to prior work.
Streaming end-to-end automatic speech recognition (ASR) systems are widely used in everyday applications that require transcribing speech to text in real-time. Their minimal latency makes them suitable for such tasks. Unlike their non-streaming counterparts, streaming models are constrained to be causal with no future context and suffer from higher word error rates (WER). To improve streaming models, a recent study [1] proposed to distill a non-streaming teacher model on unsupervised utterances, and then train a streaming student using the teachers' predictions. However, the performance gap between teacher and student WERs remains high. In this paper, we aim to close this gap by using a diversified set of non-streaming teacher models and combining them using Recognizer Output Voting Error Reduction (ROVER). In particular, we show that, despite being weaker than RNN-T models, CTC models are remarkable teachers. Further, by fusing RNN-T and CTC models together, we build the strongest teachers. The resulting student models drastically improve upon streaming models of previous work [1]: the WER decreases by 41% on Spanish, 27% on Portuguese, and 13% on French.