A novel feature-extraction for speech recognition based on multiple acoustic-feature planes

This paper describes an attempt to incorporate the functions of the auditory nerve system into the feature extractor of speech recognition. The functions include four types of well-known responses to sound stimuli: the local peaks of the steady sound spectrum, ascending FM sound, descending FM sound, and sharply rising and falling sound. Each function is realized in the form of a three-level derivative operator and is applied to a time-spectrum (TS) pattern X(t,f) of the output of the BPF with 26-channels. The resultant acoustic cue of an input speech represented by multiple acoustic-feature planes (MAFP) is compressed by using the Karhuenen-Loeve transform (KLT), then classified. In the experiments performed on a Japanese E-set (12 consonantal parts of /Ci/) extracted from continuous speech, the MAFP significantly improved the error rate from 34.5% and 29.6% obtained by X(t,f) and X(t,f)+Δ_tX(t,f) to 17.0% for unknown speakers (dimension=64)