Fast accent identification and accented speech recognition

Abstract

The performance of speech recognition systems degrades when speaker accent is different from that in the training set. Accent-independent or accent-dependent recognition both require collection of more training data. In this paper, we propose a faster accent classification approach using phoneme-class models. We also present our findings in acoustic features sensitive to a Cantonese accent, and possibly other Asian language accents. In addition, we show how we can rapidly transform a native accent pronunciation dictionary to that for accented speech by simply using knowledge of the native language of the foreign speaker. The use of this accent-adapted dictionary reduces recognition error rate by 13.5%, similar to the results obtained from a longer, data-driven process.