Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Parisa Haghani"'
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
http://arxiv.org/abs/2208.13916
Autor:
Bo Li, Ruoming Pang, Yu Zhang, Tara N. Sainath, Trevor Strohman, Parisa Haghani, Yun Zhu, Brian Farris, Neeraj Gaur, Manasa Prasad
Publikováno v:
ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::11c713ecbba0dc1a86b86ca4036fc288
https://doi.org/10.1109/icassp43922.2022.9746594
https://doi.org/10.1109/icassp43922.2022.9746594
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
Autor:
Bhuvana Ramabhadran, Isabel Leal, Yun Zhu, Manasa Prasad, Brian Farris, Neeraj Gaur, Parisa Haghani, Pedro J. Moreno Mengibar
Publikováno v:
Interspeech 2021.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a79beed82282a323a6aa89c1cede4d6c
https://doi.org/10.21437/interspeech.2021-614
https://doi.org/10.21437/interspeech.2021-614
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
https://doi.org/10.1109/icassp39728.2021.9414379
Autor:
Isabel Leal, Yun Zhu, Brian Farris, Bhuvana Ramabhadran, Neeraj Gaur, Hasim Sak, Pedro J. Moreno, Anshuman Tripathi, Qian Zhang, Parisa Haghani, Hainan Xu, Han Lu
Publikováno v:
INTERSPEECH
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a0e384d4e81248afd57d8993a8f15dc7
https://doi.org/10.21437/interspeech.2020-2847
https://doi.org/10.21437/interspeech.2020-2847
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
https://doi.org/10.1109/slt.2018.8639043
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
https://doi.org/10.1109/slt.2018.8639610
Autor:
Bo Li, Tara N. Sainath, Khe Chai Sim, Parisa Haghani, Anshuman Tripathi, Ananya Misra, Golan Pundak, Arun Narayanan, Michiel Bacchiani
Publikováno v:
INTERSPEECH
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0e1980f15272c878d98b40c40ce369a4
https://doi.org/10.21437/interspeech.2018-2246
https://doi.org/10.21437/interspeech.2018-2246
Publikováno v:
ASRU
We explore the feasibility of training long short-term memory (LSTM) recurrent neural networks (RNNs) with syllables, rather than phonemes, as outputs. Syllables are a natural choice of linguistic unit for modeling the acoustics of languages such as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9dc471d88bac930ac586102fd74268aa
https://doi.org/10.1109/asru.2017.8268932
https://doi.org/10.1109/asru.2017.8268932