Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training
Table VI: WER (%) comparison of the 6-layer-BLSTM direct PIT-CE-ASR model on the mixed speech generated from two male speakers (M + M), two female speakers (F + F) and a male and a female speaker (M + F)
['[BOLD] Genders', '[BOLD] SNR Condition', '[BOLD] High E WER', '[BOLD] Low E WER']
To better understand the results on multi-talker speech recognition, we computed the WER separately for the speech mixed with same and opposite genders. It is observed that the same-gender mixed speech is much more difficult to recognize than the opposite-gender mixed speech, and the gap is even larger when the energy ratio of the two speakers is closer to 1. It is also observed that the mixed speech of two male speakers is hard to recognize than that of two female speakers. These results suggest that effective exploitation of gender information may help to further improve the multi-talker speech recognition system. We will explore this in our future work.