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Acoustic feature extraction from speech is a fundamental part in both automatic speech recognition and automatic speaker recognition. Mel-frequency cepstral coefficients (MFCCs) are widely used in both of the above two research directions. A new feature extraction technique named perceptual MVDR-based cepstral coefficients (PMCCs) has been demonstrated to perform superior in automatic speech recognition...
Gaussian mixture models with an universal background model (UBM) have been the standard method for speaker recognition. Typically, maximum a posteriori (MAP) or maximum likelihood linear regression (MLLR) is used to adapt the means of the UBM. Together with the SVM modeling technique, these approaches can achieve excellent performance. MLLR is quite efficient when the amount of adaptation data is...
Token-based approaches have proven quite effective for spoken language identification (LID). Traditionally, Speech utterances are first decoded into token sequences, and then LID tasks are performed on these token sequences by either n-gram language models or support vector machines. In this paper, we propose a hierarchical system design, which utilizes a group of bayesian logistic regression models...
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