Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training
Table VII: WER (%) comparison of the baseline single-speaker BLSTM-RNN system and the proposed direct PIT-CE-ASR model on the IHM-3mix dataset. Diff indicates the mixed speech is from different genders, and Same indicates the mixed speech is from same gender
['[BOLD] Genders', '[BOLD] Model', '[BOLD] Speaker1', '[BOLD] Speaker2', '[BOLD] Speaker3']
The WERs on different gender combinations are also provided. Compared to the results on IHM-2mix, the results on IHM-3mix are significantly worse using the conventional single speaker model. Under this extremely hard setup, the proposed direct PIT-CE-ASR architecture still demonstrated its powerful ability on separating/tracing/recognizing the mixed speech, and achieved 25.0% relative WER reduction across all three speakers. Although the performance gap from two-talker to three-talker is obvious, it is still very promising under this speaker-independent three-talker LVCSR task. Not surprisingly, the mixed speech of different genders is relatively easier to recognize than that of same gender.