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Emotional Expression

Emotional Expression

Emotional expression encompasses how individuals convey their emotions through verbal communication and non-verbal cues. These non-verbal actions include facial expressions, body language, and physical gestures, such as frowning or smiling. Among these, facial expressions play a crucial role in emotional expression and are understood universally, indicating a biological basis for how humans communicate emotions.
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A Cantonese Audio-Visual Emotional Speech (CAVES) dataset.

Chee Seng Chong¹, Chris Davis¹, Jeesun Kim²

¹The MARCS Institute for Brain, Behaviour and Development, Western Sydney University, Locked Bag 1797, Penrith, NSW, 2751, Australia.

Behavior Research Methods

|November 28, 2023

Summary

This study introduces a new Cantonese emotional speech dataset for researching emotion expression in tonal languages. The dataset features audio-visual recordings validated for accuracy, aiding cross-linguistic emotion studies.

Keywords:

Auditory and visual expressions Cantonese dataset Dataset evaluation Emotional speech

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Area of Science:

Linguistics
Psychology
Computer Science

Background:

Tonal languages like Cantonese present unique challenges for emotion expression research.
Existing speech datasets often lack comprehensive audio-visual emotional data.

Purpose of the Study:

To introduce a novel, freely available Cantonese emotional speech dataset.
To facilitate research on the auditory and visual expression of emotion in tonal languages.
To provide a validated resource for cross-linguistic emotion perception studies.

Main Methods:

Collected audio-visual recordings of 10 native Cantonese speakers uttering 50 sentences each across six basic emotions plus neutral.
Utilized full HD (1920x1080) resolution video at 50 fps.
Conducted a validation study with 15 native Cantonese perceivers using a forced-choice emotion identification task.

Main Results:

Examined speaker and sentence variability by assessing concordance between intended and perceived emotions.
Compared findings with existing studies on spoken emotion perception in non-Cantonese languages.
The dataset's features and compilation factors were detailed.

Conclusions:

The presented Cantonese emotional speech dataset is a valuable, validated resource for scientific research.
It enables further investigation into the nuances of emotional expression in tonal languages.
The dataset's availability promotes broader research in affective computing and psycholinguistics.