Single-Channel Multi-talker Speech Recognition with Permutation Invariant Training
Table V: WER (%) of the direct PIT-CE-ASR model using different deep learning models on the IHM-2mix dataset
['[BOLD] Models', '[BOLD] SNR Condition', '[BOLD] High E WER', '[BOLD] Low E WER']
Although it still gets obvious improvement over the baseline single-speaker model, the gain is much smaller with near 20.0% WER difference in every condition than that from BLSTM-based PIT-CE-ASR model. The difference between DNN and BLSTM models partially attribute to the stronger modeling power of BLSTM models and partially attribute to the better tracing ability of RNNs. It is observed that deeper BLSTM models perform better. This is because the direct PIT-CE-ASR architecture needs to conduct two tasks - separation and recognition, and thus requires additional modeling power.