Detail publikace

Psychological Stress Detection in Speech Using Return-to-opening Phase Ratios in Glottis

Originální název

Psychological Stress Detection in Speech Using Return-to-opening Phase Ratios in Glottis

Anglický název

Psychological Stress Detection in Speech Using Return-to-opening Phase Ratios in Glottis

Jazyk

en

Originální abstrakt

This paper is focused on investigation of psychological stress in speech signal using shapes of normalised glottal pulses. The pulses were estimated by two algorithms: the Direct Inverse Filtering and Iterative and Adaptive Inverse Filtering. Normalised glottal pulses are divided into opening and return phase, and a feature vector characterizing each glottal pulse is calculated for a series of n percentage interval in time domain. Each feature vector is created by parameters describing its return to opening phase ratio, namely chosen intervals, kurtosis, skewness, and area. Further, psychological stress is detected by feature vector and four different classifiers. Experimental results show, that the best accuracy approaching 95 % is reached with Gaussian Mixture Models classifier. All the best results were obtained regarding only the interval of 5 % from both phase durations, i.e. for and after pulse peak, where the most significant differences between normal and stressed speech in feature vector are occurred. Presented experiments were performed on our own speech database containing both real stressed speech and normal speech.

Anglický abstrakt

This paper is focused on investigation of psychological stress in speech signal using shapes of normalised glottal pulses. The pulses were estimated by two algorithms: the Direct Inverse Filtering and Iterative and Adaptive Inverse Filtering. Normalised glottal pulses are divided into opening and return phase, and a feature vector characterizing each glottal pulse is calculated for a series of n percentage interval in time domain. Each feature vector is created by parameters describing its return to opening phase ratio, namely chosen intervals, kurtosis, skewness, and area. Further, psychological stress is detected by feature vector and four different classifiers. Experimental results show, that the best accuracy approaching 95 % is reached with Gaussian Mixture Models classifier. All the best results were obtained regarding only the interval of 5 % from both phase durations, i.e. for and after pulse peak, where the most significant differences between normal and stressed speech in feature vector are occurred. Presented experiments were performed on our own speech database containing both real stressed speech and normal speech.

BibTex


@article{BUT117402,
  author="Miroslav {Staněk} and Milan {Sigmund}",
  title="Psychological Stress Detection in Speech Using Return-to-opening Phase Ratios in Glottis",
  annote="This paper is focused on investigation of psychological stress in speech signal using shapes of normalised glottal pulses. The pulses were estimated by two algorithms: the Direct Inverse Filtering and Iterative and Adaptive Inverse Filtering. Normalised glottal pulses are divided into opening and return phase, and a feature vector characterizing each glottal pulse is calculated for a series of n percentage interval in time domain. Each feature vector is created by parameters describing its return to opening phase ratio, namely chosen intervals, kurtosis, skewness, and area. Further, psychological stress is detected by feature vector and four different classifiers. Experimental results show, that the best accuracy approaching 95 % is reached with Gaussian Mixture Models classifier. All the best results were obtained regarding only the interval of 5 % from both phase durations, i.e. for and after pulse peak, where the most significant differences between normal and stressed speech in feature vector are occurred. Presented experiments were performed on our own speech database containing both real stressed speech and normal speech.",
  chapter="117402",
  doi="10.5755/j01.eee.21.5.13336",
  howpublished="print",
  number="5",
  volume="21",
  year="2015",
  month="october",
  pages="59--63",
  type="journal article in Web of Science"
}