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DEPAC: a Corpus for Depression and Anxiety Detection from Speech
Automated diagnosis systems of such disorders, empowered by recent innovations in Artificial Intelligence, can pave the way to reduce the sufferings of the affected individuals.
Cost-effective Models for Detecting Depression from Speech
Our findings suggest that models trained on conventional acoustic features perform equally well or better than the ones trained on deep representation features at significantly lower computational cost, irrespective of other factors, e. g. content and length of speech, gender of the speaker and severity of the disorder.
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