Robust Text-independent Speaker Recognition with Short Utterance in Noisy Environment Using SVD as a Matching Measure

Abstract

A new technique for text-independent speaker recognition for noisy speech is presented. This technique is based on finding the ratio of the singular values of the feature vectors of the unknown speaker and each of the N reference features stored in the constructed database. The ith reference feature that gives the largest ratio is considered the feature of the unknown speaker.An overall correct recognition accuracy of 94% for clean speech and 32% for noisy speech of 0 dB SNR was obtained. A further step was conducted to enhance the noisy features by series expansion. The improvement in the recognition rate using the proposed SVD-based algorithm is compared with other distance measure algorithms. It is found that the proposed technique when cepstral features are used outperforms the conventional matching measure such as the Euclidean, the Weighted and the Mahalonobis distances, respectively.