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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. - : arXiv, 2018 BASE Show details 9 Low-Resource Contextual Topic Identification on Speech ... Liu, Chunxi; Wiesner, Matthew; Watanabe, Shinji; Harman, Craig; Trmal, Jan; Dehak, Najim; Khudanpur, Sanjeev. - : arXiv, 2018 Abstract: In topic identification (topic ID) on real-world unstructured audio, an audio instance of variable topic shifts is first broken into sequential segments, and each segment is independently classified. We first present a general purpose method for topic ID on spoken segments in low-resource languages, using a cascade of universal acoustic modeling, translation lexicons to English, and English-language topic classification. Next, instead of classifying each segment independently, we demonstrate that exploring the contextual dependencies across sequential segments can provide large improvements. In particular, we propose an attention-based contextual model which is able to leverage the contexts in a selective manner. We test both our contextual and non-contextual models on four LORELEI languages, and on all but one our attention-based contextual model significantly outperforms the context-independent models. ... : Accepted for publication at 2018 IEEE Workshop on Spoken Language Technology (SLT) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1807.06204 https://arxiv.org/abs/1807.06204 BASE Hide 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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