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Recently, using maximum likelihood linear regression (MLLR) transforms as the features for SVM based speaker recognition has been proposed. This can achieve performance comparable to that obtained with state-of-the-art approaches. In this paper, we focus on calculating the transforms based on a GMM universal background model (UBM). Rather than estimating the transforms using maximum likelihood criterion,...
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...
This paper proposes a novel feature set for robust speaker recognition, which is based on the harmonic structure of speech signals. Channel modulation effects are supposed to be weakened in the harmonic structure features, and furthermore the influence introduced by channel variability could be diminished to a certain degree. Though experiment results show that the raw performance of the harmonic...
In this paper, we present a new modeling approach for speaker recognition, which uses a kind of novel phonotactic information as the feature for S VM modeling. Gaussian mixture models (GMMs) have been proven extremely successful for text- independent speaker recognition. The GMM universal background model (UBM) is a speaker-independent model, each component of which can be considered to be modeling...
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