Publication detail

Comparison of Different Kinds of Long-Time Spectra of Voice Estimated by Modified Linear Prediction to Distinguish Speakers

SIGMUND, M.

Original Title

Comparison of Different Kinds of Long-Time Spectra of Voice Estimated by Modified Linear Prediction to Distinguish Speakers

Type

conference paper

Language

English

Original Abstract

This paper deals with two kinds of long-time spectra of speech estimated by the linear prediction approach. The standard approach usually used in short-time analysis was modified in two ways to achieve the long-time effect - either autocorrelation coefficients (AC) or predictive coefficients (PC) were averaged over a period of 2 minutes. The spectra were computed using order of prediction from 6 to 22 and evaluated in terms of diversity for a group of 17 speakers. To distinguish speakers, the most appropriate frequencies seem to be around 1010 Hz (AC averaged) or around 340 Hz (PC averaged).

Keywords

Speech signal, long-time spectrum; linear prediction; distinction of voices

Authors

SIGMUND, M.

Released

10. 10. 2019

Publisher

Romanian Academy, IEEE, EURASIP

Location

Bucharest

ISBN

978-1-7281-0983-1

Book

Proceedings of the 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD) “SpeD 2019”

Pages from

1

Pages to

6

Pages count

6

URL

https://ieeexplore.ieee.org/document/8906615

BibTex

@inproceedings{BUT159592,
  author="Milan {Sigmund}",
  title="Comparison of Different Kinds of Long-Time Spectra of Voice Estimated by Modified Linear Prediction to Distinguish Speakers",
  booktitle="Proceedings of the 2019 International Conference on Speech Technology and Human-Computer Dialogue (SpeD) “SpeD 2019”",
  year="2019",
  pages="1--6",
  publisher="Romanian Academy, IEEE, EURASIP",
  address="Bucharest",
  doi="10.1109/SPED.2019.8906615",
  isbn="978-1-7281-0983-1",
  url="https://ieeexplore.ieee.org/document/8906615"
}