Nonnative speech recognition based on bilingual model modification at state level

This paper presents a novel bilingual model modification approach to improve nonnative speech recognition accuracy when the variations of accented pronunciations occur. Each state of baseline nonnative acoustic model is modified with several candidate states from the auxiliary acoustic model, which is trained on speakers’ mother language. State mapping criterion and n-best candidates are investigated, and different numbers of Gaussian mixtures of the auxiliary acoustic model are compared based on a grammarconstrained speech recognition system. Using this bilingual model modification approach, compared to the nonnative acoustic model which has already been well trained by adaptation technique MAP, the Phrase Error Rate further achieves a 5.83% relative reduction, while only a small relative increase on Real Time Factor occurs.