Very Deep Convolutional Neural Networksfor Robust Speech Recognition
Table 4: WER (%) comparisons of the very deep CNNs joint learning with various auxiliary features. VDCNN indicates the previous best very deep CNN, and Aux indicates the auxiliary feature types
['Model', 'Aux', 'A', 'B', 'C', 'D', 'AVG']
For the auxiliary features, the use of MFCCs, fMLLR features and i-vector are explored in this work. The normal 39-dim MFCC is used with static, Δ and ΔΔ features, and 40-dim fMLLR features are generated using all the data from a given speaker. An 11-frame context window is used for MFCC and fMLLR. For the i-vector, a GMM with 2048 Gaussians is used to extract a 100-dimensional i-vector for each utterance, and these i-vectors are obtained using fMLLR features. The appropriate auxiliary feature type is important: joint learning using MFCC gets no more improvement, in contrast the adapted auxiliary features can achieve another large gain based on the advanced very deep CNN. Most of the improvements are from the three noisy subsets, so this shows that the adaptation is very important for noise robust speech recognition.