Peripheral features for HMM-based speech recognition

This paper describes an attempt to extract peripheral features of a point c(t_i,q_j) on a time-quefrency (TQ) pattern by observing n×n neighborhoods of the point, and then to incorporate these peripheral features into the MFCC-based feature extractor of a speech recognition system as a replacement to dynamic features. In the design of the feature extractor, firstly, the orthogonal bases extracted directly from speech data by using the Karhunen-Loeve transform (KLT) of 7×3 blocks on a TQ pattern are adopted as the peripheral features, then, the upper two primal bases are selected and simplified in the form of ▵_t-operator and ▵_q-operator. The proposed feature-set of MFCC and peripheral features shows significant improvements in comparison with the standard feature-set of MFCC and dynamic features in experiments with an HMM-based automatic speech recognition (ASR) system. The reason for the increased performance is discussed in terms of minimal-pair tests