Acoustic-prosodic measures discriminate the emotions of Brazilian portuguese speakers

ABSTRACT Purpose To verify if there is a difference in acoustic-prosodic measures in different emotional states of speakers of Brazilian Portuguese (BP). Methods The data sample consisted of 182 audio signals produced by actors (professionals or students), from the semi-spontaneous speech task “Look at the blue plane” in the various emotions (joy, sadness, fear, anger, surprise, disgust) and neutral emission. Values were extracted from acoustic-prosodic measures of duration, fundamental frequency and intensity of the various emotions. The Friedman comparison test was used to verify whether these measures are able to discriminate emotions. Results The prosodic-acoustic analysis revealed significant variations between emotions. The disgust emotion stood out for having the highest rate of utterance, with higher values of duration. In contrast, the joy exhibited a more accelerated speech, with lower values of duration and greater intensity. Sadness and fear were marked by lower intensity and lower frequencies, and fear presented the lowest positive asymmetry values of z-score and z-smoothed, with less elongation of the segments. Anger was highlighted by the higher vocal intensity, while surprise recorded the highest values of fundamental frequency. Conclusion The acoustic-prosodic measures proved to be effective tools for differentiating emotions in CP speakers. These parameters have great potential to discern different emotional states, broaden knowledge about vocal expressiveness and open possibilities for emotion recognition technologies with applications in artificial intelligence and mental health.

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Publication Details

Journal
CoDAS
Published
2025-01-01
DOI
https://doi.org/10.1590/2317-1782/e20240116en
Primary Topic
Emotion and Mood Recognition
Type
article
Field-Weighted Citation Impact
0.00

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article

Acoustic-prosodic measures discriminate the emotions of Brazilian portuguese speakers

Ronei Marcos de Moraes, Anna Alice Almeida, Alexandra Christine de Aguiar, Ana Carolina Constantini
CoDAS
Emotion and Mood Recognition
article

Acoustic-prosodic measures discriminate the emotions of Brazilian portuguese speakers

Ronei Marcos de Moraes, Anna Alice Almeida, Alexandra Christine de Aguiar, Ana Carolina Constantini
article en

Abstract

ABSTRACT Purpose To verify if there is a difference in acoustic-prosodic measures in different emotional states of speakers of Brazilian Portuguese (BP). Methods The data sample consisted of 182 audio signals produced by actors (professionals or students), from the semi-spontaneous speech task “Look at the blue plane” in the various emotions (joy, sadness, fear, anger, surprise, disgust) and neutral emission. Values were extracted from acoustic-prosodic measures of duration, fundamental frequency and intensity of the various emotions. The Friedman comparison test was used to verify whether these measures are able to discriminate emotions. Results The prosodic-acoustic analysis revealed significant variations between emotions. The disgust emotion stood out for having the highest rate of utterance, with higher values of duration. In contrast, the joy exhibited a more accelerated speech, with lower values of duration and greater intensity. Sadness and fear were marked by lower intensity and lower frequencies, and fear presented the lowest positive asymmetry values of z-score and z-smoothed, with less elongation of the segments. Anger was highlighted by the higher vocal intensity, while surprise recorded the highest values of fundamental frequency. Conclusion The acoustic-prosodic measures proved to be effective tools for differentiating emotions in CP speakers. These parameters have great potential to discern different emotional states, broaden knowledge about vocal expressiveness and open possibilities for emotion recognition technologies with applications in artificial intelligence and mental health.

CoDASVol. 37(4)
Universidade Federal da Paraíba (BR), Universidade Estadual de Campinas (UNICAMP) (BR)
Conselho Nacional de Desenvolvimento Científico e Tecnológico
Openalex Percentile: Top 80%
Emotion and Mood Recognition
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