Feature Normalisation for Robust Speech Recognition
(c) Cascading with HEQ
[' [BOLD] Noise Level', ' [BOLD] HEQ', ' [BOLD] NMF_plain + HEQ', ' [BOLD] NMF_robustW + HEQ']
During MFCC extraction, usually 23 LMFB coefficients are converted into 13 dimensional MFCCs through DCT. But this technique fails in high noise conditions. However, the objective of HLDA is not to achieve robustness, but class-separability. Here a method is proposed which aims at finding representations of speech feature vectors using building-blocks. Average values shown are taken over SNR levels It can be observed that HLDA can be used to achieve noise robustness in low noise levels only, and fails at high noise levels (SNR 5 to −5 dB). It can be seen that this method hardly gives an improvement over the baseline at different dictionary sizes. NMF_plain gives 1.86% absolute improvement in recognition accuracy, NMF_plain cascaded with HLDA gives 5.09%, NMF_robustW gives 5.96%, NMF_robustW+HLDA gives 8.6%. NMF_plain+HEQ gives 13.69% and NMF_robustW gives 13.48% improvement.