Noise-Robust ASR for the third ‘CHiME’ Challenge Exploiting Time-Frequency Masking based Multi-Channel Speech Enhancement and Recurrent Neural Network
Table 4: Evaluation results (WER[%]) of the combined systems.
['System', 'Dev. set Real', 'Dev. set Simu.', 'Test set Real', 'Test set Simu.']
Since the RNNME based second-pass rescoring is not included in the evaluation of acoustic modeling, the RNNME based second-pass rescoring is introduced into the two best-performed acoustic models (Maxout+dp+sMBR, LSTMP+sMBR) with the 4-gram+SP language model in one-pass decoding. Moreover, the lattice combination is performed on the two intergraded systems to achieve a better performance. The front-end used in this evaluation is MSC+PDM. The feature used in this evaluation is fMLLR+i-vector.