859 citations · 2.2k across the 80 of their papers we have counts for
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Bytes are All You Need: End-to-End Multilingual Speech Recognition and Synthesis with Bytes
Bo Li, Yu Zhang, Tara Sainath +2
We present two end-to-end models: Audio-to-Byte (A2B) and Byte-to-Audio (B2A), for multilingual speech recognition and synthesis. Prior work has predominantly used characters, sub-…
Streaming End-to-end Speech Recognition For Mobile Devices
Yanzhang He, Tara N. Sainath, Rohit Prabhavalkar +17
End-to-end (E2E) models, which directly predict output character sequences given input speech, are good candidates for on-device speech recognition. E2E models, however, present nu…
Contextual Speech Recognition with Difficult Negative Training Examples
Uri Alon, Golan Pundak, Tara N. Sainath
Improving the representation of contextual information is key to unlocking the potential of end-to-end (E2E) automatic speech recognition (ASR). In this work, we present a novel an…
Deep context: end-to-end contextual speech recognition
Golan Pundak, Tara N. Sainath, Rohit Prabhavalkar +2
In automatic speech recognition (ASR) what a user says depends on the particular context she is in. Typically, this context is represented as a set of word n-grams. In this work, w…
A Comparison of Techniques for Language Model Integration in Encoder-Decoder Speech Recognition
Shubham Toshniwal, Anjuli Kannan, Chung-Cheng Chiu +3
Attention-based recurrent neural encoder-decoder models present an elegant solution to the automatic speech recognition problem. This approach folds the acoustic model, pronunciati…