Black-box Adaptation of ASR for Accented Speech
Table 2: Overall comparison on WER and CER for Indian, Australian and British Accented Data
['Method', 'WER Ind', 'WER Aus', 'WER uk', 'CER Ind', 'CER Aus', 'CER uk']
Observe that overall the error rate of Service is lower than that of accent-adapted Local. Rover’s word-level merging provides significantly improved results than either of the two indicating that the two models exhibit complementary strengths. LM rescoring does not improve results much, establishing that the local LM may not have much impact on the improved results. Our algorithm FineMerge provides the greatest gains in WER over all methods. For Australian, we obtain a 28% relative reduction in WER over either of the service and client models. A centerpiece of our method is Viterbi aligning \vs with the frame-level character probability distribution. We show that this achieves a character-level alignment that is more accurate than existing methods by focusing only on character error rate (CER) before beam-decoding. We observe that FineMerge’s CER is much lower particularly for Indian accent. This explains that the main reasons for our gains is due to our novel frame-level fine-grained merging algorithm.