Automatic pronunciation generation by utilizing a semi-supervised deep neural networks
- Working Paper
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 Show more
Journal / seriesarXiv
SubjectSpeech recognition; Deep neural networks; Semisupervised learning; Dictionary; Sub-word unit; k-dimensional Viterbi
Organisational unit03429 - Thiele, Lothar
NotesSubmitted on 15 June 2016. See also: http://e-citations.ethbib.ethz.ch/view/pub:184869.
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