Abstract
In this paper we present a novel feature extraction algorithm based on group delay function for robust speech recognition. The modified group delay function (MODGDF) is the main feature extraction method based on group delay function, generally used for robust speech recognition. The recognition tests indicate this feature does not provide notably better results in the presence of additive noise in comparison with MFCC. In the presence of convolutional noise, the performance of MODGDF is considerably lower than MFCC. The method proposed in this paper is simple and makes more efficient utilization of the high resolution property of GDF. It is formed from three main parts which are signal modeling, GDF computation based on extracted model, and compression. The recognition results obtained over AURORA 2.0 task indicate its superior performance in comparison with MODGDF and MFCC.
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