Novel Feature Extraction for Noise Robust ASR using the Aurora 2 Database

This paper presents speech signal modeling techniques that are well suited to robust recognition of connected digits in noisy environments. After several preprocessing steps speech is represented by a block-encoding of discrete cosine transform of its spectra. In this paper we combine linear predictive coding (LPC), morphological filtering, and long block lengths to achieve robust features for improved recognition in noisy environments. The spectral envelope is first estimated by LPC. Subsequent morphological filtering enhances the peaks while smoothing the valleys, which are more affected by noise in the signal. These techniques were tested with the Aurora 2 database and the standard HMM recognizer as defined by the ETSI STQ-AURORA DSR Working group for WI007. With no major increase in computational demand a 23% word error rate (WER) reduction has been achieved as compared to the WI007 baseline MFCC front-end for multi-condition training condition. The basic conclusion is that the features resulting from the methods presented here perform better than cepstral features for ASR of noisy speech