Incremental learning for RNN-Transducer based speech recognition models

This paper investigates an incremental learning framework for a real-world voice assistant employing RNN-Transducer based automatic speech recognition (ASR) model. Such a model needs to be regularly updated to keep up with changing distribution of customer requests. We demonstrate that a simple fine-tuning approach with a combination of old and new training data can be used to incrementally update the model spending only several hours of training time and without any degradation on old data. This paper explores multiple rounds of incremental updates on the ASR model with monthly training data. Results show that the proposed approach achieves 5-6% relative WER improvement over the models trained from scratch on the monthly evaluation datasets. In addition, we explore if it is pos-sible to improve recognition of specific new words. We simulate multiple rounds of incremental updates with handful of training utterances per word (both real and synthetic) and show that the recognition of the new words improves dramatically but with a minor degradation on general data. Finally, we demonstrate that the observed degradation on general data can be mitigated by interleaving monthly updates with updates targeting specific words.