Abstract
This paper presents the methodology we applied for building a new corpus of Bulgarian speech suitable for training and evaluating modern speech recognition systems. The Bulgarian Parliament ASR (BG-PARLAMA) corpus is derived from the recordings of the plenary sessions of the Bulgarian Parliament. The manually transcribed texts and the audio data of the speeches are processed automatically to build an aligned and segmented corpus. NLP tools and resources for Bulgarian are utilized for the language specific tasks. The resulting corpus consists of 249 hours of speech from 572 speakers and is freely available for academic use. First experiments with an ASR system trained on the BG-PARLAMA corpus have been conducted showing word error rate of around 7% on parliament speeches from unseen speakers using time-delay deep neural network (TD-DNN) architecture. The BG-PARLAMA corpus is to our knowledge the largest speech corpus currently available for Bulgarian.
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Notes
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ISLRN:250-105-856-478-2 http://www.islrn.org/resources/250-105-856-478-2/.
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ISLRN:755-406-235-455-4 http://www.islrn.org/resources/755-406-235-455-4/.
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In our setup training with audio for individual speakers limited to 60Â min did not improve the ASR accuracy.
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Acknowledgments
The research presented in this paper is partially funded by the Bulgarian Ministry of Education and Science via grant DO1-200/2018 ’Electronic healthcare in Bulgaria’ (e-Zdrave). We acknowledge the provided access to the e-infrastructure of the Centre for Advanced Computing and Data Processing, with the financial support by the Grant No BG05M2OP001-1.001-0003, financed by the Science and Education for Smart Growth Operational Program (2014–2020) and co-financed by the European Union through the European structural and Investment funds.
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Geneva, D., Shopov, G., Mihov, S. (2019). Building an ASR Corpus Based on Bulgarian Parliament Speeches. In: MartÃn-Vide, C., Purver, M., Pollak, S. (eds) Statistical Language and Speech Processing. SLSP 2019. Lecture Notes in Computer Science(), vol 11816. Springer, Cham. https://doi.org/10.1007/978-3-030-31372-2_16
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