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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    • "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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