Do End-to-End Speech Recognition Models Care About Context?

Lasse Borgholt, Jakob D. Havtorn, Željko Agić, Anders Søgaard, Lars Maaløe, Christian Igel

The two most common paradigms for end-to-end speech recognition are connectionist temporal classification (CTC) and attention-based encoder-decoder (AED) models. It has been argued that the latter is better suited for learning an implicit language model. We test this hypothesis by measuring temporal context sensitivity and evaluate how the models perform when we constrain the amount of contextual information in the audio input. We find that the AED model is indeed more context sensitive, but that the gap can be closed by adding self-attention to the CTC model. Furthermore, the two models perform similarly when contextual information is constrained. Finally, in contrast to previous research, our results show that the CTC model is highly competitive on WSJ and LibriSpeech without the help of an external language model.

 DOI: 10.21437/Interspeech.2020-1750

Cite as: Borgholt, L., Havtorn, J.D., Agić, Ž., Søgaard, A., Maaløe, L., Igel, C. (2020) Do End-to-End Speech Recognition Models Care About Context?. Proc. Interspeech 2020, 4352-4356, DOI: 10.21437/Interspeech.2020-1750.

  author={Lasse Borgholt and Jakob D. Havtorn and Željko Agić and Anders Søgaard and Lars Maaløe and Christian Igel},
  title={{Do End-to-End Speech Recognition Models Care About Context?}},
  booktitle={Proc. Interspeech 2020},