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We present a novel approach to automatic speaker age classification, which combines regression and classification to achieve competitive classification accuracy on telephone speech. Support vector machine regression is used to generate finer age estimates, which are combined with the posterior probabilities of well-trained discriminative gender classifiers to predict both the age and gender of a speaker...
In the last years the speaker recognition field has made extensive use of speaker adaptation techniques. Adaptation allows speaker model parameters to be estimated using less speech data than needed for maximum-likelihood (ML) training. The maximum a posteriori (MAP) and maximum-likelihood linear regression (MLLR) techniques have typically been used for adaptation. Recently, MAP and MLLR adaptation...
In this paper, we study speaker characterization using prosodic supervectors with negative within-class covariance normalization (NWCCN) projection and speaker modeling with support vector regression (SVR). We also propose a segmental weight fusion (SWF) technique that combines acoustic and prosodic subsystems effectively, despite the big performance gap between the subsystems. We validate the effectiveness...
Maximum likelihood linear regression (MLLR) is a widely used technique for speaker adaptation in large vocabulary speech recognition system. Recently, using MLLR transforms as features for SVM based speaker recognition tasks has been proposed, achieving performance comparable to that obtained with cepstral features. In this paper, we focus on calculating the transforms based on a GMM universal background...
Mel-frequency cepstral coefficients (MFCC) are proved to be the effective feature for speech recognition and speaker recognition, while pitch frequency is also one of the favorite prosodic features. This work manages to bridge them with hidden Markov model (HMM) and support vector regression (SVR). A set of speaker-independent HMMs is used to align the training data, so that the parameters of the...
One particularly difficult challenge for cross-channel MLLR (CMLLR) are two widely-used techniques for speaker introduced in the 2005 and 2006 NIST Speaker Recognition Evaluations, where training uses telephone speech and verification uses speech from multiple auxiliary comparable to that obtained with cepstral features. This paper describes a new feature extraction technique for speaker recognition...
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