A method of accent modeling through Pronunciation Dictionary Adaptation (PDA) is presented. We derive the pronunciation variation between canonical speaker groups and accent groups and add an encoding of the differences to a canonical dictionary to create a new, adapted dictionary that reflects the accent characteristics. The pronunciation variation information is then integrated with acoustic and language models into a one-pass search framework. It is assumed that acoustic deviation and pronunciation variation are independent but complementary phenomena that cause poor performance among accented speakers. Therefore, MLLR, an efficient model adaptation technique, is also presented both alone and in combination with DA. It is shown that when PDA, MLLR and PDA+MLLR are used, error rate reductions of 13.9%, 24.1% and 28.4% respectively are achieved.

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