Using Discrete Probabilities With Bhattacharyya Measure for SVM-Based Speaker Verification

Inst. for Infocomm Res., Agency for Sci., Technol. & Res. (A*STAR), Singapore, Singapore
IEEE Transactions on Audio Speech and Language Processing (Impact Factor: 2.48). 06/2011; 19(4):861 - 870. DOI: 10.1109/TASL.2010.2064308
Source: IEEE Xplore


Support vector machines (SVMs), and kernel classifiers in general, rely on the kernel functions to measure the pairwise similarity between inputs. This paper advocates the use of discrete representation of speech signals in terms of the probabilities of discrete events as feature for speaker verification and proposes the use of Bhattacharyya coefficient as the similarity measure for this type of inputs to SVM. We analyze the effectiveness of the Bhattacharyya measure from the perspective of feature normalization and distribution warping in the SVM feature space. Experiments conducted on the NIST 2006 speaker verification task indicate that the Bhattacharyya measure outperforms the Fisher kernel, term frequency log-likelihood ratio (TFLLR) scaling, and rank normalization reported earlier in literature. Moreover, the Bhattacharyya measure is computed using a data-independent square-root operation instead of data-driven normalization, which simplifies the implementation. The effectiveness of the Bhattacharyya measure becomes more apparent when channel compensation is applied at the model and score levels. The performance of the proposed method is close to that of the popular GMM supervector with a small margin.

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Available from: Kong Aik Lee, Oct 05, 2015
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    • "The models are intended to represent some language-dependent characteristics seen on the training data. Depending on the information sources they model, these models could be 1) stochastic, e.g., Gaussian mixture model (GMM) [149], [150] and hidden Markov model (HMM) [98], [149]; 2) deterministic, e.g., vector quantization (VQ) [125], support vector machine (SVM) [16], [19], [31], [65], and neural network [7]; or 3) discrete stochastic, e.g., n-gram [40], [46], [66], [67]. During the test phase, a test utterance is compared to each of the language-dependent models after going through the same preprocessing and feature extraction step. "
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    ABSTRACT: Spoken language recognition refers to the automatic process through which we determine or verify the identity of the language spoken in a speech sample. We study a computational framework that allows such a decision to be made in a quantitative manner. In recent decades, we have made tremendous progress in spoken language recognition, which benefited from technological breakthroughs in related areas, such as signal processing, pattern recognition, cognitive science, and machine learning. In this paper, we attempt to provide an introductory tutorial on the fundamentals of the theory and the state-of-the-art solutions, from both phonological and computational aspects. We also give a comprehensive review of current trends and future research directions using the language recognition evaluation (LRE) formulated by the National Institute of Standards and Technology (NIST) as the case studies.
    Proceedings of the IEEE 05/2013; 101(5):1136-1159. DOI:10.1109/JPROC.2012.2237151 · 4.93 Impact Factor
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    INTERSPEECH 2011, 12th Annual Conference of the International Speech Communication Association, Florence, Italy, August 27-31, 2011; 01/2011
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    ABSTRACT: Recently, joint factor analysis (JFA) and identity-vector (i-vector) represent the dominant techniques used for speaker recognition due to their superior performance. Developed relatively earlier, the Gaussian mixture model - support vector machine (GMM-SVM) with nuisance attribute projection (NAP) has gradually become less popular. However, when developing the relevance factor in maximum a posteriori (MAP) estimation of GMM to be adapted by application data in place of the conventional fixed value, it is noted that GMM-SVM demonstrates some advantages. In this paper, we conduct a comparative study between GMM-SVM with adaptive relevance factor and JFA/i-vector under the framework of Speaker Recognition Evaluation (SRE) formulated by the National Institute of Standards and Technology (NIST).
    Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on; 10/2013
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