184 citations · 185 across the 5 of their papers we have counts for
5 papers
Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices
Leonid Velikovich, Christopher Li, Diamantino Caseiro +4
For end-to-end Automatic Speech Recognition (ASR) models, recognizing personal or rare phrases can be hard. A promising way to improve accuracy is through spelling correction (or r…
Deferred NAM: Low-latency Top-K Context Injection via Deferred Context Encoding for Non-Streaming ASR
Zelin Wu, Gan Song, Christopher Li +9
Contextual biasing enables speech recognizers to transcribe important phrases in the speaker's context, such as contact names, even if they are rare in, or absent from, the trainin…
High-precision Voice Search Query Correction via Retrievable Speech-text Embedings
Christopher Li, Gary Wang, Kyle Kastner +9
Automatic speech recognition (ASR) systems can suffer from poor recall for various reasons, such as noisy audio, lack of sufficient training data, etc. Previous work has shown that…
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm
Weiran Wang, Zelin Wu, Diamantino Caseiro +10
Contextual biasing refers to the problem of biasing the automatic speech recognition (ASR) systems towards rare entities that are relevant to the specific user or application scena…
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…