o What we have experimented Hierarchical Feature Weighting Each speech recognition unit(SRU) sub-set shares the same and fixed feature weighting vector (using same feature components) The sru set is divided into subsets according to a Minimum Classification Error(MCE)criterion Sub-band feature weighting inside MfcC calculation s The weight in each sub-band is based on its snr level The method is combined with the noise spectral Subtraction Center of speech Technology, Tsinghua University Slide 14Center of Speech Technology, Tsinghua University Slide 14 ❑ What we have experimented ❖ Hierarchical Feature Weighting, ➢ Each speech recognition unit (SRU) sub-set shares the same and fixed feature weighting vector (using same feature components); ➢ The SRU set is divided into subsets according to a Minimum Classification Error (MCE) criterion. ❖ Sub-band feature weighting inside MFCC calculation ➢ The weight in each sub-band is based on its SNR level. ➢ The method is combined with the noise spectral subtraction