Asr systems in noisy environment: Auditory features based on gammachirp filter using the AURORA database

This paper deals with the analysis of Automatic Speech Recognition (ASR) suitable for usage within noisy environment in various conditions. Recent research has shown that auditory features based on gammachirp filterbank (GF) are promising to improve robustness of ASR systems against noise. The behavior of parameterization techniques was analyzed from the viewpoint of robustness against noise. It was done for Mel Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), Gammachirp Filterbank Cepstral Coefficient (GFCC) and Gammachirp Filterbank Perceptual Linear Prediction (GF-PLP). GFCC features have shown best recognition efficiency for clean as well as for noisy database. GFCC and GF-PLP features are calculated using Matlab and saved in HTK format. Training and testing for speech recognition is done using HTK. The above-mentioned techniques were tested with impulsive signals within AURORA databases.