184 citations · 515 across the 17 of their papers we have counts for
19 papers
Language model fusion for streaming end to end speech recognition
Rodrigo Cabrera, Xiaofeng Liu, Mohammadreza Ghodsi +3
Streaming processing of speech audio is required for many contemporary practical speech recognition tasks. Even with the large corpora of manually transcribed speech data available…
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Tara N. Sainath, Yanzhang He, Bo Li +26
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i.e., word error rate (WER), and latency, i.e…
Language-agnostic Multilingual Modeling
Arindrima Datta, Bhuvana Ramabhadran, Jesse Emond +2
Multilingual Automated Speech Recognition (ASR) systems allow for the joint training of data-rich and data-scarce languages in a single model. This enables data and parameter shari…
A comparison of end-to-end models for long-form speech recognition
Chung-Cheng Chiu, Wei Han, Yu Zhang +11
End-to-end automatic speech recognition (ASR) models, including both attention-based models and the recurrent neural network transducer (RNN-T), have shown superior performance com…
Large-Scale Multilingual Speech Recognition with a Streaming End-to-End Model
Anjuli Kannan, Arindrima Datta, Tara N. Sainath +6
Multilingual end-to-end (E2E) models have shown great promise in expansion of automatic speech recognition (ASR) coverage of the world's languages. They have shown improvement over…
Extracting Symptoms and their Status from Clinical Conversations
Nan Du, Kai Chen, Anjuli Kannan +3
This paper describes novel models tailored for a new application, that of extracting the symptoms mentioned in clinical conversations along with their status. Lack of any publicly…
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
Jonathan Shen, Patrick Nguyen, Yonghui Wu +88
Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models. Lingvo models a…
On the Choice of Modeling Unit for Sequence-to-Sequence Speech Recognition
Kazuki Irie, Rohit Prabhavalkar, Anjuli Kannan +3
In conventional speech recognition, phoneme-based models outperform grapheme-based models for non-phonetic languages such as English. The performance gap between the two typically…
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…
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…
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…
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…
Speech recognition for medical conversations
Chung-Cheng Chiu, Anshuman Tripathi, Katherine Chou +11
In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations ($1…
Adversarial Evaluation of Dialogue Models
Anjuli Kannan, Oriol Vinyals
The recent application of RNN encoder-decoder models has resulted in substantial progress in fully data-driven dialogue systems, but evaluation remains a challenge. An adversarial…
Smart Reply: Automated Response Suggestion for Email
Anjuli Kannan, Karol Kurach, Sujith Ravi +8
In this paper we propose and investigate a novel end-to-end method for automatically generating short email responses, called Smart Reply. It generates semantically diverse suggest…