Teager energy based feature parameters for robust speech recognition in car noise

Abstract

In this paper, a new set of speech feature parameters based on multirate signal processing and the Teager Energy Operator is developed. The speech signal is first divided into nonuniform subbands in mel-scale using a multirate filter-bank, then the Teager energies of the subsignals are estimated. Finally, the feature vector is constructed by log-compression and inverse DCT computation. The new feature parameters have a robust speech recognition performance in car engine noise which is low pass in nature.