Text-independent speaker identification in noisy environment using singular value decomposition

A new technique for text-independent speaker identification 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 i^th reference feature that gives the largest ratio is considered as the feature of the unknown speaker. An overall correct recognition accuracy of 99.5% for clean speech and 77.5% for noisy speech of 0 dB SNR was obtained. It was found that, for clean and noisy speech of 15 dB SNR or less, the proposed technique outperforms the conventional matching measure such as the Euclidean and the weighted distances, respectively.