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Neural Computing and Applications

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28.10.2019 | Developing nature-inspired intelligence by neural systems

An ICA-based method for stress classification from voice samples

verfasst von: Daniel Palacios, Victoria Rodellar, Carlos Lázaro, Andrés Gómez, Pedro Gómez

Erschienen in: Neural Computing and Applications | Ausgabe 24/2020

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Abstract

Emotion detection is a hot topic nowadays for its potential application to intelligent systems in different fields such as neuromarketing, dialogue systems, friendly robotics, vending platforms and amiable banking. Nevertheless, the lack of a benchmarking standard makes it difficult to compare results produced by different methodologies, which could help the research community improve existing approaches and design new ones. Besides, there is the added problem of accurate dataset production. Most of the emotional speech databases and associated documentation are either privative or not publicly available. Therefore, in this work, two stress-elicited databases containing speech from male and female speakers were recruited, and four classification methods are compared in order to detect and classify speech under stress. Results from each method are presented to show their quality performance, besides the final scores attained, in what is a novel approach to the field of study.

Vorheriger Artikel Multilingual audio information management system based on semantic knowledge in complex environments

Nächster Artikel Integration of textual cues for fine-grained image captioning using deep CNN and LSTM

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Titel: An ICA-based method for stress classification from voice samples
verfasst von: Daniel Palacios
Victoria Rodellar
Carlos Lázaro
Andrés Gómez
Pedro Gómez
Publikationsdatum: 28.10.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 24/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-019-04549-3

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