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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 – 7 of 7 1 KIT Lecture Translator: Multilingual Speech Translation with One-Shot Learning Nguyen, Thai-Son; Zenkel, Thomas; Waibel, Alex. - : Association for Computational Linguistics, 2022 BASE Show details 2 Lecture Translator Speech translation framework for simultaneous lecture translation Waibel, Alex; Nguyen, Thai-Son; Cho, Eunah. - : Association for Computational Linguistics, 2022 BASE Show details 3 Open Source Toolkit for Speech to Text Translation Zenkel, Thomas; Sperber, Matthias; Niehues, Jan... In: The Prague Bulletin of Mathematical Linguistics, 111 (1), 125–135 ; ISSN: 0032-6585, 1804-0462 (2022) BASE Show details 4 Neural Language Codes for Multilingual Acoustic Models ... Müller, Markus; Stüker, Sebastian; Waibel, Alex. - : arXiv, 2018 BASE Show details 5 Multilingual Adaptation of RNN Based ASR Systems ... Müller, Markus; Stüker, Sebastian; Waibel, Alex. - : arXiv, 2017 BASE Show details 6 Phonemic and Graphemic Multilingual CTC Based Speech Recognition ... Müller, Markus; Stüker, Sebastian; Waibel, Alex. - : arXiv, 2017 Abstract: Training automatic speech recognition (ASR) systems requires large amounts of data in the target language in order to achieve good performance. Whereas large training corpora are readily available for languages like English, there exists a long tail of languages which do suffer from a lack of resources. One method to handle data sparsity is to use data from additional source languages and build a multilingual system. Recently, ASR systems based on recurrent neural networks (RNNs) trained with connectionist temporal classification (CTC) have gained substantial research interest. In this work, we extended our previous approach towards training CTC-based systems multilingually. Our systems feature a global phone set, based on the joint phone sets of each source language. We evaluated the use of different language combinations as well as the addition of Language Feature Vectors (LFVs). As contrastive experiment, we built systems based on graphemes as well. Systems having a multilingual phone set are known to ... Keyword: Artificial Intelligence cs.AI; Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering URL: https://dx.doi.org/10.48550/arxiv.1711.04564 https://arxiv.org/abs/1711.04564 BASE Hide details 7 Evaluation of Crowdsourced User Input Data for Spoken Dialog Systems Wagner, Martin; Stüker, Sebastian; Werner, Steffen. - : Association for Computational Linguistics, 2015 BASE Show details

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