Stress Level Detection in Continuous Speech Using CNNs and a Hybrid Attention Layer

Title

Stress Level Detection in Continuous Speech Using CNNs and a Hybrid Attention Layer

Creator

Subramani R.; Suresh K.; Donald A.C.; Sivaselvan K.

Description

This paper mainly targets stress detection by analyzing the audio signals obtained from human beings. Deep learning is used to model the levels of stress pertaining to this whole paper followed by an analysis of the Mel spectrogram of the audio signals is done. A hybrid attention model helps us achieve the required result. The dataset that has been used for this article is the DAIC-WOZ dataset containing continuous speech files of conversations between a patient and a virtual assistant who is controlled by a human counselor from another room. The best results obtained were a 78.7% accuracy on the classification of the stress levels. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

Source

Lecture Notes in Networks and Systems, Vol-731 LNNS, pp. 367-382.

Date

2024-01-01

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

DAIC-WOZ; Hybrid attention model; Speech emotion recognition; Stress levels

Coverage

Subramani R., Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, 560029, India; Suresh K., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, 560029, India; Donald A.C., Department of Computer Science, CHRIST (Deemed to be University), Bengaluru, 560029, India; Sivaselvan K., Department of Mathematics, St. Thomas College of Arts and Science, Chennai, 600107, India

Rights

Restricted Access

Relation

ISSN: 23673370; ISBN: 978-981994070-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-99-4071-4_29

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85177093870&doi=10.1007%2f978-981-99-4071-4_29&partnerID=40&md5=5530921ffd82034602f62f66faeb4165