Feature-space SVM adaptation for speaker adapted word prominence detection

Schnall, Andrea; Heckmann, Martin

doi:10.1016/j.csl.2018.06.001

Cited by 5 publications

(1 citation statement)

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“…In text-independent speaker verification, support vector machine (SVM) has been proven to be effective classifier and most popularly used for many years. 4 It has many desirable properties inherently, including the ability to classify patterns with least expected risk principle, to classify sparse data 5 without over-training problem, and to make non-linear decisions via kernel function. Sloin and Burshtein 6 presented a discriminative training algorithm based on SVM to improve the classification of hidden Markov models.…”

Section: Introductionmentioning

confidence: 99%

Speaker verification normalization sequence kernel based on Gaussian mixture model super-vector and Bhattacharyya distance

Xing

Tan

Wang

2019

Journal of Low Frequency Noise, Vibration and Active Control

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Due to the accessibility and economy of human speech, speaker verification has become the research hotspot in the field of biometric authentication. A novel normalization sequence kernel based on Bhattacharyya distance clustering and within class covariance normalization was proposed in this paper. In this kernel, the high computation complexity and channel interference susceptibility that commonly exist in speaker verification could be restrained. In our method, we calculated the Bhattacharyya distance between pairs of Gaussian mixture models first. And then, a clustering algorithm was designed according to Gaussian mixture model’s Bhattacharyya distance to obtain clustering center models. Maximum a posteriori was applied on these clustering center models to generate super-vectors immediately following. The sequence kernel was generated based on Bhattacharyya distance transformation and super-vectors. Finally, within class covariance normalization was utilized to restrain the channel distortion in kernel space. We adopted support vector machine as classifier to decide the target speaker. The experiment results on TIMIT corpus and NIST 2008 SRE showed that our proposed kernel has superior recognition accuracy and better robustness.

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Section: Introductionmentioning

confidence: 99%