On the Use of Different Feature Extraction Methods for Linear and Non Linear kernels
This work addresses speaker identification for speech recognition systems, but it is incremental as it compares existing methods without introducing new ones.
The paper tackled the problem of robust speech recognition by comparing different feature extraction methods and normalization techniques for speaker identification, finding that the combination of GMM and SVM kernels improved performance.
The speech feature extraction has been a key focus in robust speech recognition research; it significantly affects the recognition performance. In this paper, we first study a set of different features extraction methods such as linear predictive coding (LPC), mel frequency cepstral coefficient (MFCC) and perceptual linear prediction (PLP) with several features normalization techniques like rasta filtering and cepstral mean subtraction (CMS). Based on this, a comparative evaluation of these features is performed on the task of text independent speaker identification using a combination between gaussian mixture models (GMM) and linear and non-linear kernels based on support vector machine (SVM).