Auditory inspired Acoustic Model for Hybrid ASR System using Gammatone based Gabor Filters
Spectro temporal features have recently shown much performance improvement for robust Automatic Speech Recognition (ASR) tasks. Gabor filters are best known to extract the spectro temporal cues of speech. Spectro temporal representation becomes an essential ingredient for two dimensional Gabor based feature extraction methods. State of the art spectro temporal features is mostly based on Mel spectrogram. However, the time frequency representation based on the Mel scale is not accurate enough to model the human auditory system. This paper concentrates on obtaining the spectro temporal representation by incorporating a physiologically and psychoacoustically motivated gammatone filter called gammatonegram. From literature, gammatonegram is found to better approximate the auditory perception of speech. The spectro temporal features obtained using gammatonegram based Gabor filters are fed to a hybrid Deep Neural Network (DNN) Hidden Markov Model (HMM) framework to develop the acoustic model of an ASR system. Experimental analysis is carried out with NOISEX 92 database implemented on TIMIT. The experimental results show the better performance gain obtained with the proposed features compared with conventional feature extraction methods.
2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT)