Deep Learning Techniques for Speech Emotion Recognition, from Databases to Models.

Abbaschian, Babak Joze; Sierra-Sosa, Daniel; Elmaghraby, Adel

Abbaschian, Babak Joze; Sierra-Sosa, Daniel; Elmaghraby, Adel.

Afiliación

Abbaschian BJ; Computer Science and Engineering Department, University of Louisville Louisville, KY 40292, USA.
Sierra-Sosa D; Computer Science and Engineering Department, University of Louisville Louisville, KY 40292, USA.
Elmaghraby A; Computer Science and Engineering Department, University of Louisville Louisville, KY 40292, USA.

Sensors (Basel) ; 21(4)2021 Feb 10.

Article en En | MEDLINE | ID: mdl-33578714

RESUMEN

The advancements in neural networks and the on-demand need for accurate and near real-time Speech Emotion Recognition (SER) in human-computer interactions make it mandatory to compare available methods and databases in SER to achieve feasible solutions and a firmer understanding of this open-ended problem. The current study reviews deep learning approaches for SER with available datasets, followed by conventional machine learning techniques for speech emotion recognition. Ultimately, we present a multi-aspect comparison between practical neural network approaches in speech emotion recognition. The goal of this study is to provide a survey of the field of discrete speech emotion recognition.

Palabras clave

CNN; GAN; LSTM; attention mechanism; autoencoders; deep learning; emotional speech database; machine learning; speech emotion recognition

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sensors (Basel) Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

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