184 citations · 515 across the 17 of their papers we have counts for
5 papers · 1 filter
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…