Abstract
This paper deals with the development of language specific modules (lexicons, phonetic inventories, LMs and AMs) for Russian, Ukrainian and Belarusian (used by 260M, 45M and 3M native speakers, respectively). Instead of working on each language separately, we adopt a common approach that allows us to share data and tools, yet taking into account language unique features. We utilize only freely available text and audio data that can be found on web pages of major newspaper and broadcast publishers. This must be done with large care, as the 3 languages are often mixed in spoken and written media. So, one component of the automated training process is a language identification module. At the output of the complete process there are 3 pronunciation lexicons (each about 300K words), 3 partly shared phoneme sets, and corresponding acoustic (DNN) and language (N-gram) models. We employ them in our media monitoring system and provide results achieved on a test set made of several complete TV news in all the 3 languages. The WER values vary in range from 24 to 36%.
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Acknowledgments
The research was supported by the Technology Agency of the Czech Republic (project TA04010199) and by the Student Grant Scheme (SGS) at the Technical University of Liberec.
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Safarik, R., Nouza, J. (2017). Unified Approach to Development of ASR Systems for East Slavic Languages. In: Camelin, N., Estève, Y., MartÃn-Vide, C. (eds) Statistical Language and Speech Processing. SLSP 2017. Lecture Notes in Computer Science(), vol 10583. Springer, Cham. https://doi.org/10.1007/978-3-319-68456-7_16
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DOI: https://doi.org/10.1007/978-3-319-68456-7_16
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