Deferred NAM: Low-latency Top-K Context Injection via Deferred Context Encoding for Non-Streaming ASR

no code implementations • 15 Apr 2024 • Zelin Wu, Gan Song, Christopher Li, Pat Rondon, Zhong Meng, Xavier Velez, Weiran Wang, Diamantino Caseiro, Golan Pundak, Tsendsuren Munkhdalai, Angad Chandorkar, Rohit Prabhavalkar

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 training data.

High-precision Voice Search Query Correction via Retrievable Speech-text Embedings

no code implementations • 8 Jan 2024 • Christopher Li, Gary Wang, Kyle Kastner, Heng Su, Allen Chen, Andrew Rosenberg, Zhehuai Chen, Zelin Wu, Leonid Velikovich, Pat Rondon, Diamantino Caseiro, Petar Aleksic

In this paper, we eliminate the hypothesis-audio mismatch problem by querying the correction database directly using embeddings derived from the utterance audio; the embeddings of the utterance audio and candidate corrections are produced by multimodal speech-text embedding networks trained to place the embedding of the audio of an utterance and the embedding of its corresponding textual transcript close together.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm

no code implementations • 29 Sep 2023 • Weiran Wang, Zelin Wu, Diamantino Caseiro, Tsendsuren Munkhdalai, Khe Chai Sim, Pat Rondon, Golan Pundak, Gan Song, Rohit Prabhavalkar, Zhong Meng, Ding Zhao, Tara Sainath, Pedro Moreno Mengibar

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 scenarios.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1