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Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks ...
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Abstract:
Phonemic or phonetic sub-word units are the most commonly used atomic elements to represent speech signals in modern ASRs. However they are not the optimal choice due to several reasons such as: large amount of effort required to handcraft a pronunciation dictionary, pronunciation variations, human mistakes and under-resourced dialects and languages. Here, we propose a data-driven pronunciation estimation and acoustic modeling method which only takes the orthographic transcription to jointly estimate a set of sub-word units and a reliable dictionary. Experimental results show that the proposed method which is based on semi-supervised training of a deep neural network largely outperforms phoneme based continuous speech recognition on the TIMIT dataset. ... : Proc. of 17th Interspeech (2016), San Francisco, California, USA ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Sound cs.SD
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URL: https://arxiv.org/abs/1606.05007
https://dx.doi.org/10.48550/arxiv.1606.05007
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The SIWIS database: a multilingual speech database with acted emphasis
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In: http://infoscience.epfl.ch/record/218855 (2016)
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