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Speaker identification through use of features selected using genetic algorithm

Speaker identification through use of features selected using genetic algorithm

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The authors introduce the use of a genetic algorithm in the reduction of a 24 parameter (12 LPC derived cepstral coefficients + 12 Δ-cepstral coefficients) set to a five, six, seven, eight or ten parameter set, for each speaker in text-independent speaker identification. The experimental results show that there is ~5% increase in the recognition rate when the reduced set of parameters is used.

Inspec keywords: feature extraction; speaker recognition; genetic algorithms

Other keywords: text-independent speaker identification; parameter set; feature selection; LPC; genetic algorithm; cepstral coefficients

Subjects: Optimisation techniques; Optimisation techniques; Speech and audio signal processing; Speech recognition

References

    1. 1)
      • S.M. Morgera , L. Datta . Toward a fundamental theory of optimal feature selection : Part I. IEEE Trans. Acoust., Speech, Signal Process. , 601 - 616
    2. 2)
      • H.B. Bidsaria . Least desirable feature elimination in a general pattern recognitionproblem. Pattern Recogn. , 365 - 370
    3. 3)
      • Haydar, A., Demirekler, M., Nakipoglu, B., Thzun, B.: `Text independent speaker identification using bayes decision rule andvector quantizer', COMCON5, 5th Int. Conf. on Advances in Communication and Control, 1995, p. 56–60.
    4. 4)
      • L.R. Rabiner , R.W. Schafer . (1987) Digital processing of speech signals.
    5. 5)
      • A. Cohen , I. Froind . On text-independent speaker identification using a quadratic classifierwith optimal features. Speech Commun. , 1 , 35 - 44
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