431 citations · 786 across the 13 of their papers we have counts for
7 papers · 1 filter
An analysis of incorporating an external language model into a sequence-to-sequence model
Anjuli Kannan, Yonghui Wu, Patrick Nguyen +3
Attention-based sequence-to-sequence models for automatic speech recognition jointly train an acoustic model, language model, and alignment mechanism. Thus, the language model comp…
No Need for a Lexicon? Evaluating the Value of the Pronunciation Lexica in End-to-End Models
Tara N. Sainath, Rohit Prabhavalkar, Shankar Kumar +9
For decades, context-dependent phonemes have been the dominant sub-word unit for conventional acoustic modeling systems. This status quo has begun to be challenged recently by end-…
Minimum Word Error Rate Training for Attention-based Sequence-to-Sequence Models
Rohit Prabhavalkar, Tara N. Sainath, Yonghui Wu +4
Sequence-to-sequence models, such as attention-based models in automatic speech recognition (ASR), are typically trained to optimize the cross-entropy criterion which corresponds t…
Improving the Performance of Online Neural Transducer Models
Tara N. Sainath, Chung-Cheng Chiu, Rohit Prabhavalkar +4
Having a sequence-to-sequence model which can operate in an online fashion is important for streaming applications such as Voice Search. Neural transducer is a streaming sequence-t…
Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model
Bo Li, Tara N. Sainath, Khe Chai Sim +6
Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM),…
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
Chung-Cheng Chiu, Tara N. Sainath, Yonghui Wu +11
Attention-based encoder-decoder architectures such as Listen, Attend, and Spell (LAS), subsume the acoustic, pronunciation and language model components of a traditional automatic…