Development of dialect-specific speech recognizers using adaptation methods

Several adaptation approaches have been proposed in an effort to improve the speech recognition performance in mismatched conditions. However, the application of these approaches had been mostly constrained to the speaker or channel adaptation tasks. We first investigate the effect of mismatched dialects between training and testing speakers in an automatic speech recognition (ASR) system. We find that a mismatch in dialects significantly influences the recognition accuracy. Consequently, we apply several adaptation approaches to develop a dialect-specific recognition system using a dialect-dependent system trained on a different dialect and a small number of training sentences from the target dialect. We show that adaptation improves the recognition performance dramatically with small amounts of training sentences. We further show that, although the recognition performance of traditionally trained systems highly degrades as we decrease the number of training speakers, the performance of adapted systems is not influenced so much