Automatic Pronunciation Generation by Utilizing a Semi-Supervised Deep Neural Networks
Naoya Takahashi, Tofigh Naghibi, Beat Pfister · 2016
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.