Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks
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.
Cite
@article{arxiv.1606.05007,
title = {Automatic Pronunciation Generation by Utilizing a Semi-supervised Deep Neural Networks},
author = {Naoya Takahashi and Tofigh Naghibi and Beat Pfister},
journal= {arXiv preprint arXiv:1606.05007},
year = {2016}
}
Comments
Proc. of 17th Interspeech (2016), San Francisco, California, USA