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This paper proposes a speech/music classification system based on i-vector. An analysis of two classification methods, namely cosine distance score (CDS) and support vector machine (SVM) is performed. Two session compensation methods, within-class covariance normalization (WCCN) and linear discriminant analysis (LDA) are also discussed. The performance of proposed systems yields better results compared...
Non-negative Matrix Factorization (NMF) has been widely studied and applied to variant computer vision tasks, such as image clustering and pattern classification. Meanwhile, real world stimuli for human neural system (e.g., face images) are usually represented as high-dimensional data vectors rely on graph embedding in original Euclidean space. Thus, the traditional NMF and its variants exhibit weakness...
This paper develops a system to automatically distinguish natural speech from synthetic speech. The issue of feature selection is considered. We take commonly used feature Mel-Frequency Cepstrum Coefficient (MFCC) in consideration, as well as other features such as Relative Phase Shift (RPS) and pitch tuned for Automatically Speech Recognition (ASR). We found some features are complimentary in the...
The purpose of this paper is to develop an intelligent algorithm by integrating the Kernel Independent Component Analysis (KICA) and the Support Vector Machines (SVM) for forecasting the steel temperature. Characterized by nonlinearity, multivariable, coupling of the heating furnace, it is necessary to feature extraction. Thus, this study proposes the application of KICA to extract the hidden information...
This paper explores the use of constrained maximum likelihood linear regression (CMLLR) transforms as features for language recognition. Modeling is carried out through support vector machine (SVM). This work proposes a novel CMLLR supervector kernel. Results on the NIST LRE09 task show that feature-domain CMLLR transforms contain more language dependent information than model-domain MLLRs, and the...
Considering of solving the bottleneck problem of computing ability, power consumption, size and weight of mobile robot with image acquisition ability, a compact and embedded image capture device based on ARM and CMOS image sensor is developed in this paper. Its hardware structure, software design and some key image preprocessing programs are also described in detail. Experiments show that it can not...
Channel variability is the major cause of performance degradation in text-independent speaker verification. Compensation technology in feature, model or score domain has been widely applied to baseline systems to mitigate mismatch. Newly proposed Gaussian mixture models super vector-support vector machine (GMM-SVM or GSV-SVM) baseline system has proven successful through integrating advantages of...
In this paper, we describe two approaches for language identification (LID) using support vector machines (SVM) and phonetic n-gram. One is to use the language model scores of phone sequences to do SVM training. The other is to use the n-gram probabilities of those phones to train SVM models. For the second approach, we propose a new effective normalization method. In the experiments of 30 s test...
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