Do end-to-end speech recognition models care about context?
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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.
|Title of host publication||Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH|
|Publisher||International Speech Communication Association (ISCA)|
|Publication status||Published - 2020|
|Event||21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020 - Shanghai, China|
Duration: 25 Oct 2020 → 29 Oct 2020
|Conference||21st Annual Conference of the International Speech Communication Association, INTERSPEECH 2020|
|Periode||25/10/2020 → 29/10/2020|
|Sponsor||Alibaba Group, Amazon Alexa, Apple, et al., Intel, Magic Data|
- Attention-based encoder-decoder, Automatic speech recognition, Connectionist temporal classification, End-to-end speech recognition
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