Text-Independent speaker identification in phoneme-independent subspace using PCA transformation
Haoze Lu, Masafumi Nishida, Yasuo Horiuchi, Shingo Kuroiwa · International Journal of Biometrics · 2010
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.