Speaker identification using probabilistic PCA model selection

Gaussian mixture model (GMM) techniques are popular for speaker identification. Theoretically, each Gaussian function should have a full covariance matrix. However, the diagonal covariance matrix is usually used because the inverse of diagonal covariance matrix can be easily calculated via expectation maximization (EM) algorithm. This paper proposes a new probabilistic principal component analysis (PPCA) model for speaker identification. The full covariance of speaker’s data is considered. This model is originated from factor analysis theory. The probability distributions using PPCA are well defined. In particular, GMM and PPCA are found to be equivalent when using diagonal covariance matrix. In this study, we derive a novel PPCA model selection and establish models for different speakers. Applying PPCA model selection, we can dynamically determine the numbers of speech features and mixture components. Experiments show that PPCA achieves desirable speaker recognition performance with proper model regularization.