Noise-Robust ASR for the third ‘CHiME’ Challenge Exploiting Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent Neural Network
Table 1: Evaluation results (WER[%]) of the proposed speech enhancement front-ends with the baseline acoustic model (GMM-HMM) and language model (3-gram). “CAL” means phase calibration.
['Speech Enh.', 'Dev. set Real', 'Dev. set Simu.', 'Test set Real', 'Test set Simu.']
Comparing with the baseline using noisy data, the performance of the baseline with speech enhancement front-end is greatly improved on simulated test set, but degraded on real test set. This phenomenon shows that mismatch between enhanced real and simulated data is introduced by the baseline beamforming based speech enhancement process. The system performance on the real test set would be greatly influenced by this mismatch, since relatively large amount of simulated data is used in the training phase.