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Findings of the IWSLT 2020 Evaluation campaign
Niehues, Jan; Federico, Marcello; Ma, Xutai. - : Association for Computational Linguistics, 2022
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KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning
Nguyen, Thai-Son; Zenkel, Thomas; Waibel, Alex. - : Association for Computational Linguistics, 2022
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Lecture Translator Speech translation framework for simultaneous lecture translation
Waibel, Alex; Nguyen, Thai-Son; Cho, Eunah. - : Association for Computational Linguistics, 2022
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4
Open Source Toolkit for Speech to Text Translation
In: The Prague Bulletin of Mathematical Linguistics, 111 (1), 125–135 ; ISSN: 0032-6585, 1804-0462 (2022)
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Lightly Supervised Quality Estimation
Waibel, Alex; Niehues, Jan; Stüker, Sebastian. - : Association for Computational Linguistics, 2022
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6
ELITR Multilingual Live Subtitling: Demo and Strategy ...
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Efficient Weight factorization for Multilingual Speech Recognition ...
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8
Efficientweight factorization for multilingual speech recognition
Pham, Ngoc-Quan; Nguyen, Tuan-Nam; Stueker, Sebastian. - : Curran Associates, Inc., 2021
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Findings of the IWSLT 2020 Evaluation campaign ...
Ansari, Ebrahim; Axelrod, Amittai; Bach, Nguyen. - : Association for Computational Linguistics, 2020
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10
Super-Human Performance in Online Low-latency Recognition of Conversational Speech ...
Abstract: Achieving super-human performance in recognizing human speech has been a goal for several decades, as researchers have worked on increasingly challenging tasks. In the 1990's it was discovered, that conversational speech between two humans turns out to be considerably more difficult than read speech as hesitations, disfluencies, false starts and sloppy articulation complicate acoustic processing and require robust handling of acoustic, lexical and language context, jointly. Early attempts with statistical models could only reach error rates over 50% and far from human performance (WER of around 5.5%). Neural hybrid models and recent attention-based encoder-decoder models have considerably improved performance as such contexts can now be learned in an integral fashion. However, processing such contexts requires an entire utterance presentation and thus introduces unwanted delays before a recognition result can be output. In this paper, we address performance as well as latency. We present results for a system ... : To appear in Interspeech 2021 ...
Keyword: Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences
URL: https://dx.doi.org/10.48550/arxiv.2010.03449
https://arxiv.org/abs/2010.03449
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11
Neural Language Codes for Multilingual Acoustic Models ...
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Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the "Speaking Rosetta" JSALT 2017 Workshop ...
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13
19: Grundlagen der Automatischen Spracherkennung, Vorlesung, WS 2017/18, 24.01.2018
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14
Open Source Toolkit for Speech to Text Translation
In: Prague Bulletin of Mathematical Linguistics , Vol 111, Iss 1, Pp 125-135 (2018) (2018)
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15
Multilingual Adaptation of RNN Based ASR Systems ...
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16
Phonemic and Graphemic Multilingual CTC Based Speech Recognition ...
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17
Comparison of Decoding Strategies for CTC Acoustic Models ...
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18
13: Grundbegriffe der Informatik, Vorlesung, WS 2017/18, 01.12.2017
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19
Grundlagen der Automatischen Spracherkennung, Vorlesung, WS 2016/17, 18.01.2017, 18
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03: Grundlagen der Automatischen Spracherkennung, Vorlesung, WS 2017/18, 30.10.2017
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