Improving RNN Transducer Modeling for End to End Speech Recognition
In the last few years, an emerging trend in automatic speech recognition research is the study of end to end (E2E) systems. Connectionist Temporal Classification (CTC) Attention Encoder Decoder (AED) and RNN Transducer (RNN T) are the most popular three methods. Among these three methods, RNN T has the advantages to do online streaming which is challenging to AED and it doesn't have CTC's frame independence assumption. In this paper, we improve the RNN T training in two aspects. First, we optimize the training algorithm of RNN T to reduce the memory consumption so that we can have larger training minibatch for faster training speed. Second, we propose better model structures so that we obtain RNN T models with the very good accuracy but small footprint. Trained with 30 thousand hours anonymized and transcribed Microsoft production data, the best RNN T model with even smaller model size (216 Megabytes) achieves up to 11.8% relative word error rate (WER) reduction from the baseline RNN T model. This best RNN T model is significantly better than the device hybrid model with similar size by achieving up to 15.0% relative WER reduction, and obtains similar WERs as the server hybrid model of 5120 Megabytes in size.
2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU)