Speaker identification and verification using Gaussian mixture speaker models
Speech Communication
Survey of the state of the art in human language technology
Survey of the state of the art in human language technology
Unsupervised speaker recognition based on competition between self-organizing maps
IEEE Transactions on Neural Networks
Robust speaker identification in the presence of car noise
International Journal of Biometrics
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In this paper we proposed a text-independent (TI) speaker identification method that suppresses the phonetic information by a subspace method, under the assumption that a subspace with large variance in the speech feature space is a 'phoneme-dependent subspace' and a complementary subspace of it is a 'phoneme-independent subspace'. Principal Component Analysis (PCA) is employed to construct these subspaces. Gaussian Mixture Model (GMM)-based speaker identification experiments using both the phonetic information suppressed feature and the conventional Mel-Frequency Ceptrum Coefficient (MFCC) were carried out. As a result, the proposed method has been proven to be effective for decreasing the identification error rates.