ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network

Janne Pylkkönen, Antti Ukkonen, Juho Kilpikoski, Samu Tamminen, Hannes Heikinheimo

Adaption of end-to-end speech recognition systems to new tasks is known to be challenging. A number of solutions have been proposed which apply external language models with various fusion methods, possibly with a combination of two-pass decoding. Also TTS systems have been used to generate adaptation data for the end-to-end models. In this paper we show that RNN-transducer models can be effectively adapted to new domains using only small amounts of textual data. By taking advantage of model’s inherent structure, where the prediction network is interpreted as a language model, we can apply fast adaptation to the model. Adapting the model avoids the need for complicated decoding time fusions and external language models. Using appropriate regularization, the prediction network can be adapted to new domains while still retaining good generalization capabilities. We show with multiple ASR evaluation tasks how this method can provide relative gains of 10–45% in target task WER. We also share insights how RNN-transducer prediction network performs as a language model.

doi: 10.21437/Interspeech.2021-1191

Cite as: Pylkkönen, J., Ukkonen, A., Kilpikoski, J., Tamminen, S., Heikinheimo, H. (2021) Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network. Proc. Interspeech 2021, 1882-1886, doi: 10.21437/Interspeech.2021-1191

  author={Janne Pylkkönen and Antti Ukkonen and Juho Kilpikoski and Samu Tamminen and Hannes Heikinheimo},
  title={{Fast Text-Only Domain Adaptation of RNN-Transducer Prediction Network}},
  booktitle={Proc. Interspeech 2021},