  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.
