Detail publikace

Statistical Analysis of Fundamental Frequency Based Features in Speech under Stress

Originální název

Statistical Analysis of Fundamental Frequency Based Features in Speech under Stress

Anglický název

Statistical Analysis of Fundamental Frequency Based Features in Speech under Stress

Jazyk

en

Originální abstrakt

A significant part of the non-linguistic information carried in speech refers to the speaker and his/her internal state. This study investigates sixteen features based on fundamental frequency of speech F0 in order to detect stress in speakers. The total frequency ranges of F0 across specific short-time speech segments having stable F0 values were evaluated as the best features for speaker-independent stress detection. F0 contours were computed frame-by-frame using an optimized autocorrelation function. In our experiments, we used utterances spoken by 14 male speakers and taken from own database of speech under real psychological stress.

Anglický abstrakt

A significant part of the non-linguistic information carried in speech refers to the speaker and his/her internal state. This study investigates sixteen features based on fundamental frequency of speech F0 in order to detect stress in speakers. The total frequency ranges of F0 across specific short-time speech segments having stable F0 values were evaluated as the best features for speaker-independent stress detection. F0 contours were computed frame-by-frame using an optimized autocorrelation function. In our experiments, we used utterances spoken by 14 male speakers and taken from own database of speech under real psychological stress.

Dokumenty

BibTex


@article{BUT102578,
  author="Milan {Sigmund}",
  title="Statistical Analysis of Fundamental Frequency Based Features in Speech under Stress",
  annote="A significant part of the non-linguistic information carried in speech refers to the speaker and his/her internal state. This study investigates sixteen features based on fundamental frequency of speech F0 in order to detect stress in speakers. The total frequency ranges of F0 across specific short-time speech segments having stable F0 values were evaluated as the best features for speaker-independent stress detection. F0 contours were computed frame-by-frame using an optimized autocorrelation function. In our experiments, we used utterances spoken by 14 male speakers and taken from own database of speech under real psychological stress.",
  chapter="102578",
  number="3",
  volume="42",
  year="2013",
  month="september",
  pages="286--291",
  type="journal article - other"
}