Investigation of speech-based language-independent possibilities of depression recognition

The presented research examined whether it is possible to use the same method to create different language-specific models with similar performance, and whether it is possible to implement language-independent recognition of depression. A depression database one in German and one in Hungarian were u...

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Bibliographic Details
Published in2022 45th International Conference on Telecommunications and Signal Processing (TSP) pp. 226 - 229
Main Author Kiss, Gabor
Format Conference Proceeding
LanguageEnglish
Published IEEE 13.07.2022
Subjects
cross-lingual
Depression
Feature extraction
Signal processing
speech
Support vector machines
SVMs
Telecommunications
x-vector
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DOI10.1109/TSP55681.2022.9851347

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Summary:The presented research examined whether it is possible to use the same method to create different language-specific models with similar performance, and whether it is possible to implement language-independent recognition of depression. A depression database one in German and one in Hungarian were used to perform the experiments. The x-vector architecture published by Snyder et al. was used for feature extraction, and Support Vector Regression was used to predict the severity of depression. Classification (depressed / healthy) based on regression results was also implemented. Monolingual and multilingual experiments were performed. Based on the results, it can be stated that it is possible to create different language models with similar performance using the same method. Furthermore, it can be stated that it is possible to create a model valid for multiple languages. Research is currently at an early stage. In the future, it is necessary to expand the number of speech databases used in the experiments.
DOI:10.1109/TSP55681.2022.9851347