Word-Level Speech Recognition With a Letter to Word Encoder

Ronan Collobert · Awni Hannun · Gabriel Synnaeve

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

[ Abstract ]
Tue 14 Jul noon PDT — 12:45 p.m. PDT
Tue 14 Jul 11 p.m. PDT — 11:45 p.m. PDT


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.

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