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Speaker recognition using continuous density support vector machines

Speaker recognition using continuous density support vector machines

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A new classification system for text-independent speaker recognition is presented. This system combines the output probabilities of generative Gaussian mixture models and discriminative classifier support vector machines. The new method is tested on the YOHO database and achieves superior performance.

Inspec keywords: learning automata; pattern classification; speaker recognition

Other keywords: generative Gaussian mixture models; discriminative classifier support vector machines; classification system; continuous density support vector machines; YOHO database; output probabilities; text-independent speaker recognition

Subjects: Learning in AI (theory); Speech recognition; Speech recognition and synthesis; Automata theory

References

    1. 1)
      • K.R. Farrell , R.J. Mammone , K.T. Assaleh . Speaker recognition using neural networks and conventional classifiers. IEEE Trans. Speech Audio Process.
    2. 2)
      • M.D.T. Jaakkola , D. Haussler . A discriminative framework for detecting remote protein homologies. J. Comput. Biol.
    3. 3)
      • C. Cortes , V. Vapnik . Support vector networks. Mach. Learn. , 273 - 297
    4. 4)
      • Farrell, K., Kosonocky, S., Mammone, R.: `Neural tree network/vector quantization probability estimators for speakerrecognition', Neural Networks for Signal Processing IV, Proc. 1994 IEEE Workshop, 1994.
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