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Poster
Word-Level Speech Recognition With a Letter to Word Encoder
Ronan Collobert · Awni Hannun · Gabriel Synnaeve

Tue Jul 14 12:00 PM -- 12:45 PM & Tue Jul 14 11:00 PM -- 11:45 PM (PDT) @
in Poster Session 6 »

We propose a direct-to-word sequence model which uses a word network to learn word embeddings from letters. The word network can be integrated seamlessly with arbitrary sequence models including Connectionist Temporal Classification and encoder-decoder models with attention. We show our direct-to-word model can achieve word error rate gains over sub-word level models for speech recognition. We also show that our direct-to-word approach retains the ability to predict words not seen at training time without any retraining. Finally, we demonstrate that a word-level model can use a larger stride than a sub-word level model while maintaining accuracy. This makes the model more efficient both for training and inference.

Keywords: Algorithms · Architectures · Deep Sequence Models · Speech Processing · Applications - Language, Speech and Dialog

Author Information

Ronan Collobert (Facebook AI Research)
Awni Hannun (Facebook AI Research)
Gabriel Synnaeve (Facebook AI Research)

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