184 citations · 194 across the 4 of their papers we have counts for
8 papers
Improving Proper Noun Recognition in End-to-End ASR By Customization of the MWER Loss Criterion
Cal Peyser, Tara N. Sainath, Golan Pundak
Proper nouns present a challenge for end-to-end (E2E) automatic speech recognition (ASR) systems in that a particular name may appear only rarely during training, and may have a pr…
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…
Phoneme-Based Contextualization for Cross-Lingual Speech Recognition in End-to-End Models
Ke Hu, Antoine Bruguier, Tara N. Sainath +2
Contextual automatic speech recognition, i.e., biasing recognition towards a given context (e.g. user's playlists, or contacts), is challenging in end-to-end (E2E) models. Such mod…
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…
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…