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Discovering a discriminative feature representative together with a suitable distance measure is the key for a successful speaker recognition system. In this paper, we propose a new approach for automatic speaker verification. The main contribution of the paper is the extraction of discriminative speaker features using non-negative matrix factorization (NMF) decomposition in the GMM mean space, and...
The process of audio signal classification (ASC) involves the extraction of features from sound and the use of these features to identify the class it belongs to. There are many possible applications for ASC including for example speech recognition, audio database creation and information retrieval, health condition monitoring, audio scene analysis, etc. While relevant features have been well studied...
In this paper, a new method is proposed based on the side information and non-dominated sorting evolution strategy (NSES)-based K-means clustering algorithm. In a distance metric learning approach, data points are transformed to a new space where the Euclidean distances between similar and dissimilar points are at their minimum and maximum, respectively. However, the NSES-based K-means clustering...
This paper proposes a novel Subband Energy (SBE) distance measure to describe the differences between heterogeneous segments, and applies it in multi-pass speech/non-speech discrimination. The first pass of the discrimination is a segmentation stage based on Bayesian Information Criterion (BIC). The second pass is a classification stage employing a Gaussian Mixture Model (GMM) classifier. The third...
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