Speech Emotion Recognition by Conventional Machine Learning andDeep Learning

  1. Lope, Javier de 1
  2. Enrique Hernández 1
  3. Vanessa Vargas 1
  4. Manuel Graña 2
  1. 1 Universidad Politécnica de Madrid
    info

    Universidad Politécnica de Madrid

    Madrid, España

    ROR https://ror.org/03n6nwv02

  2. 2 Universidad del País Vasco/Euskal Herriko Unibertsitatea
    info

    Universidad del País Vasco/Euskal Herriko Unibertsitatea

    Lejona, España

    ROR https://ror.org/000xsnr85

Libro:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Año de publicación: 2021

Páginas: 319-330

Congreso: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Tipo: Aportación congreso

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Resumen

This paper reports experimental results of speech emotion recognition by conventional machine learning methods and deep learning techniques. We use a selection of mel frequency cepstral coefficients (MFCCs) as features for the conventional machine learning classifiers. The convolutional neural network uses as features the mel spectrograms treated as images. We test both approaches over a state of the art free database that provides samples of 8 emotions recorded by 24 professional actors. We report and comment the accuracy achieved by each classifier in cross validation experiments. Results of our proposal are competitive with recent studies.

Fuente de los datos: Dialnet