Detail publikace

Speaker Discrimination Using Long-Term Spectrum of Speech

Originální název

Speaker Discrimination Using Long-Term Spectrum of Speech

Anglický název

Speaker Discrimination Using Long-Term Spectrum of Speech

Jazyk

en

Originální abstrakt

In this article, a specific long-term speech spectrum was investigated with respect to its use for speaker recognition. The long-term spectrum was calculated by means of second-order linear prediction using the average autocorrelation coefficients. Four subbands with the most discriminative capability were selected for speaker recognition. These subbands involve the frequencies of 0-1.2 kHz in total. The best recognition rates, i.e. 91.7% on complete speech and 100% on voiced speech, were achieved in optimal paired subbands.

Anglický abstrakt

In this article, a specific long-term speech spectrum was investigated with respect to its use for speaker recognition. The long-term spectrum was calculated by means of second-order linear prediction using the average autocorrelation coefficients. Four subbands with the most discriminative capability were selected for speaker recognition. These subbands involve the frequencies of 0-1.2 kHz in total. The best recognition rates, i.e. 91.7% on complete speech and 100% on voiced speech, were achieved in optimal paired subbands.

BibTex


@article{BUT159590,
  author="Milan {Sigmund}",
  title="Speaker Discrimination Using Long-Term Spectrum of Speech",
  annote="In this article, a specific long-term speech spectrum was investigated with respect to its use for speaker recognition. The long-term spectrum was calculated by means of second-order linear prediction using the average autocorrelation coefficients. Four subbands with the most discriminative capability were selected for speaker recognition. These subbands involve the frequencies of 0-1.2 kHz in total. The best recognition rates, i.e. 91.7% on complete speech and 100% on voiced speech, were achieved in optimal paired subbands.",
  address="Kaunas University of Technology",
  chapter="159590",
  doi="10.5755/j01.itc.48.3.21248",
  institution="Kaunas University of Technology",
  number="3",
  volume="48",
  year="2019",
  month="september",
  pages="446--453",
  publisher="Kaunas University of Technology",
  type="journal article in Web of Science"
}