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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 10 of 10 1 The Multilingual TEDx Corpus for Speech Recognition and Translation ... Salesky, Elizabeth; Wiesner, Matthew; Bremerman, Jacob. - : arXiv, 2021 BASE Show details 2 End-to-end ASR to jointly predict transcriptions and linguistic annotations ... NAACL 2021 2021; Fujita, Yuya; Omachi, Motoi. - : Underline Science Inc., 2021 BASE Show details 3 A Corpus for Large-Scale Phonetic Typology ... Salesky, Elizabeth; Chodroff, Eleanor; Pimentel, Tiago. - : arXiv, 2020 BASE Show details 4 A Corpus for Large-Scale Phonetic Typology ... Salesky, Elizabeth; Chodroff, Eleanor; Pimentel, Tiago. - : ETH Zurich, 2020 BASE Show details 5 A corpus for large-scale phonetic typology Salesky, Elizabeth; Chodroff, Eleanor; Pimentel, Tiago. - 2020 BASE Show details 6 A Corpus for Large-Scale Phonetic Typology Chodroff, Eleanor; Cotterell, Ryan; Wiesner, Matthew... In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (2020) BASE Show details 7 Massively Multilingual Adversarial Speech Recognition ... Adams, Oliver; Wiesner, Matthew; Watanabe, Shinji. - : arXiv, 2019 BASE Show details 8 Analysis of Multilingual Sequence-to-Sequence speech recognition systems ... Karafiát, Martin; Baskar, Murali Karthick; Watanabe, Shinji; Hori, Takaaki; Wiesner, Matthew; Černocký, Jan "Honza''. - : arXiv, 2018 Abstract: This paper investigates the applications of various multilingual approaches developed in conventional hidden Markov model (HMM) systems to sequence-to-sequence (seq2seq) automatic speech recognition (ASR). On a set composed of Babel data, we first show the effectiveness of multi-lingual training with stacked bottle-neck (SBN) features. Then we explore various architectures and training strategies of multi-lingual seq2seq models based on CTC-attention networks including combinations of output layer, CTC and/or attention component re-training. We also investigate the effectiveness of language-transfer learning in a very low resource scenario when the target language is not included in the original multi-lingual training data. Interestingly, we found multilingual features superior to multilingual models, and this finding suggests that we can efficiently combine the benefits of the HMM system with the seq2seq system through these multilingual feature techniques. ... : arXiv admin note: text overlap with arXiv:1810.03459 ... Keyword: Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.1811.03451 https://arxiv.org/abs/1811.03451 BASE Hide details 9 Low-Resource Contextual Topic Identification on Speech ... Liu, Chunxi; Wiesner, Matthew; Watanabe, Shinji. - : arXiv, 2018 BASE Show details 10 Multilingual sequence-to-sequence speech recognition: architecture, transfer learning, and language modeling ... Cho, Jaejin; Baskar, Murali Karthick; Li, Ruizhi. - : arXiv, 2018 BASE Show details

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