CONF Imseng_INTERSPEECH-2_2010/IDIAP Towards mixed language speech recognition systems Imseng, David Bourlard, Hervé Magimai-Doss, Mathew EXTERNAL https://publications.idiap.ch/attachments/papers/2010/Imseng_INTERSPEECH-2_2010.pdf PUBLIC https://publications.idiap.ch/index.php/publications/showcite/Imseng_Idiap-RR-15-2010 Related documents Proceedings of Interspeech Makuhari, Japan 2010 September 2010 278-281 Multilingual speech recognition obviously involves numerous research challenges, including common phoneme sets, adaptation on limited amount of training data, as well as mixed language recognition (common in many countries, like Switzerland). In this latter case, it is not even possible to assume that one knows in advance the language being spoken. This is the context and motivation of the present work. We indeed investigate how current state-of-the-art speech recognition systems can be exploited in multilingual environments, where the language (from an assumed set of five possible languages, in our case) is not a priori known during recognition. We combine monolingual systems and extensively develop and compare different features and acoustic models. On SpeechDat(II) datasets, and in the context of isolated words, we show that it is actually possible to approach the performances of monolingual systems even if the identity of the spoken language is not a priori known. REPORT Imseng_Idiap-RR-15-2010/IDIAP Towards mixed language speech recognition systems Imseng, David Bourlard, Hervé Magimai-Doss, Mathew EXTERNAL https://publications.idiap.ch/attachments/reports/2010/Imseng_Idiap-RR-15-2010.pdf PUBLIC Idiap-RR-15-2010 2010 Idiap July 2010 Multilingual speech recognition obviously involves numerous research challenges, including common phoneme sets, adaptation on limited amount of training data, as well as mixed language recognition (common in many countries, like Switzerland). In this latter case, it is not even possible to assume that one knows in advance the language being spoken. This is the context and motivation of the present work. We indeed investigate how current state-of-the-art speech recognition systems can be exploited in multilingual environments, where the language (from an assumed set of 5 possible languages, in our case) is not a priori known during recognition. We combine monolingual systems and extensively develop and compare different features and acoustic models. On SpeechDat(II) datasets, and in the context of isolated words, we show that it is actually possible to approach performances of monolingual systems even if the identity of the spoken language is not a priori known.