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Gaussian mixture models (GMMs) are commonly used in text-independent speaker verification for modeling the spectral distribution of speech. Recent studies have shown the effectiveness of characterizing speaker information using the mean super-vector obtained by concatenating the mean vectors of the GMM. This paper proposes to use the spatial correlation captured by the covariance matrix of the mean...
This paper extends our previous work on large margin estimation (LME) of GMM parameters with extend Baum-Welch (EBW) for spoken language recognition. To overcome the problem in the LME that negative samples in the training set are not used in parameter estimation, we propose a soft margin estimation (SME) method in this paper. The soft margin is scaled by a loss function measuring the distance between...
This paper extends our previous work on feature transformation-based support vector machines for speaker recognition by proposing a joint MAP adaptation of feature transformation (FT) and Gaussian Mixture Models (GMM) parameters. In the new approach, the prior probability density functions (PDFs) of FT and GMM parameters are jointly estimated using the background data under the maximum likelihood...
In this paper, a new feature selection method for speaker recognition is proposed to keep the high quality speech frames for speaker modelling and to remove noisy and corrupted speech frames. In order to obtain robust voice activity detection in variety of acoustic conditions, the spectral subtraction algorithm is adopted to estimate the frame power. An energy based frame selection algorithm is then...
In this paper, we propose a self-organized clustering method for feature mapping to compensate the channel variation in spoken language recognition. The self-organized clustering is realized by transforming the utterances into the Gaussian mixture model (GMM) supervectors and categorizing the supervectors through k-mean algorithm. Based on the language-dependent cluster-of-utterance information of...
We propose novel approaches for optimizing the detection performance in spoken language recognition. Two objective functions are designed to directly relate model parameters to two performance metrics of interest, the detection cost function and the area under the detection-error-tradeoff curve, respectively. Both metrics are approximated with differentiable functions of model parameters by using...
In this paper we propose a generalized feature transformation approach to compensating for channel variation in speaker verification (SV) applications. Channel-dependent (CD) piecewise linear transformations are used for feature compensation. CD transformation parameters are estimated together with a channel-independent (CI) root Gaussian mixture model (GMM) from training data with a variety of channel...
This paper presents a method to extract tone relevant features based on pitch flux from continuous speech signal. The autocorrelations of two adjacent frames are calculated and the covariance between them is estimated to extract multi-dimensional pitch flux features. These features, together with MFCCs, are modeled in a 2-stream GMM models, and are tested in a 3-dialect identification task for Chinese...
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