Výsledky vyhledávání - "Caseiro, Diamantino"

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Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices

Autor: Velikovich, Leonid, Li, Christopher, Caseiro, Diamantino, Kumar, Shankar, Rondon, Pat, Joshi, Kandarp, Velez, Xavier

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 rewriting) of the ASR lattice, where potentially misrecognized phrases

Externí odkaz: http://arxiv.org/abs/2409.16469

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

Autor: Wu, Zelin, Song, Gan, Li, Christopher, Rondon, Pat, Meng, Zhong, Velez, Xavier, Wang, Weiran, Caseiro, Diamantino, Pundak, Golan, Munkhdalai, Tsendsuren, Chandorkar, Angad, Prabhavalkar, Rohit

Publikováno v: 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics - Industry Track

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. Attention-based biasing is a leading approach which allows for

Externí odkaz: http://arxiv.org/abs/2404.10180

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

Autor: Li, Christopher, Wang, Gary, Kastner, Kyle, Su, Heng, Chen, Allen, Rosenberg, Andrew, Chen, Zhehuai, Wu, Zelin, Velikovich, Leonid, Rondon, Pat, Caseiro, Diamantino, Aleksic, Petar

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 recall can be improved by retrieving rewrite candidates from a large

Externí odkaz: http://arxiv.org/abs/2401.04235

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Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm

Autor: Wang, Weiran, Wu, Zelin, Caseiro, Diamantino, Munkhdalai, Tsendsuren, Sim, Khe Chai, Rondon, Pat, Pundak, Golan, Song, Gan, Prabhavalkar, Rohit, Meng, Zhong, Zhao, Ding, Sainath, Tara, Mengibar, Pedro Moreno

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. We propose algorithms for contextual biasing based on the Knuth-

Externí odkaz: http://arxiv.org/abs/2310.00178

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