Výsledky vyhledávání - "Parisa Haghani"

A Language Agnostic Multilingual Streaming On-Device ASR System

Autor: Bo Li, Tara Sainath, Ruoming Pang, Shuo-Yiin Chang, Qiumin Xu, Trevor Strohman, Vince Chen, Qiao Liang, Heguang Liu, Yanzhang He, Parisa Haghani, Sameer Bidichandani

On-device end-to-end (E2E) models have shown improvements over a conventional model on English Voice Search tasks in both quality and latency. E2E models have also shown promising results for multilingual automatic speech recognition (ASR). In this p

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e69d8dfd3d8f4389b06eed47e083c02c
http://arxiv.org/abs/2208.13916

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Streaming End-to-End Multilingual Speech Recognition with Joint Language Identification

Autor: Chao Zhang, Bo Li, Tara Sainath, Trevor Strohman, Sepand Mavandadi, Shuo-Yiin Chang, Parisa Haghani

Language identification is critical for many downstream tasks in automatic speech recognition (ASR), and is beneficial to integrate into multilingual end-to-end ASR as an additional task. In this paper, we propose to modify the structure of the casca

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::48bd4fe6333011053c8e723968f9842b

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Mixture of Informed Experts for Multilingual Speech Recognition

Autor: Bhuvana Ramabhadran, Yun Zhu, Parisa Haghani, Manasa Prasad, Neeraj Gaur, Brian Farris, Isabel Leal, Pedro J. Moreno

Publikováno v: ICASSP

When trained on related or low-resource languages, multilingual speech recognition models often outperform their monolingual counterparts. However, these models can suffer from loss in performance for high resource or unrelated languages. We investig

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::d4205ae4fe16abbb58129dc57eebe263
https://doi.org/10.1109/icassp39728.2021.9414379

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Leveraging Language ID in Multilingual End-to-End Speech Recognition

Autor: Pedro J. Moreno, Zhongdi Qu, Parisa Haghani, Austin Waters, Neeraj Gaur

Publikováno v: ASRU

Recent advances in end-to-end speech recognition have made it possible to build multilingual models, capable of recognizing speech in multiple languages. Multilingual models can outperform their monolingual counterparts, depending on the amount of tr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::110b246adf13c06fdee5992e4097f166
https://doi.org/10.1109/asru46091.2019.9003870

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Toward Domain-Invariant Speech Recognition via Large Scale Training

Autor: Khe Chai Sim, Mohamed G. Elfeky, Trevor Strohman, Ananya Misra, Michiel Bacchiani, Arun Narayanan, Anshuman Tripathi, Golan Pundak, Parisa Haghani

Publikováno v: SLT

Current state-of-the-art automatic speech recognition systems are trained to work in specific `domains', defined based on factors like application, sampling rate and codec. When such recognizers are used in conditions that do not match the training d

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6f8761113c076ea769bce14469f25b5
https://doi.org/10.1109/slt.2018.8639610

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From Audio to Semantics: Approaches to End-to-End Spoken Language Understanding

Autor: Arun Narayanan, Galen Chuang, Zhongdi Qu, Rohit Prabhavalkar, Neeraj Gaur, Parisa Haghani, Pedro J. Moreno, Michiel Bacchiani, Austin Waters

Publikováno v: SLT

Conventional spoken language understanding systems consist of two main components: an automatic speech recognition module that converts audio to a transcript, and a natural language understanding module that transforms the resulting text (or top N hy

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d1b2db66c60a737a66d9d88c906e0de7
https://doi.org/10.1109/slt.2018.8639043

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