Simple Meta-optimization of the Feature MFCC for Public Emotional Datasets Classification

Enrique Antonio de la Cal Marín; Alberto Gallucci; José Ramón Villar Flecha; Kaori Yoshida; Mario Koeppen

Ayuda

Simple Meta-optimization of the Feature MFCC for Public Emotional Datasets Classification

Cal, Enrique de la ^[1] ; Alberto Gallucci ^[1] ; Jose Ramón Villar ^[1] ; Kaori Yoshida ^[2] ; Mario Koeppen ^[2]
1. [1] Universidad de Oviedo
  
  Universidad de Oviedo
  
  Oviedo, España
2. [2] Kyushu Institute of Technology
  
  Kyushu Institute of Technology
  
  Kokurakita Ku, Japón
Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López, Pablo García Bringas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2021, ISBN 978-3-030-86271-8, págs. 659-670
Idioma: inglés
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Resumen
- A Speech Emotion Recognition (SER) system can be defined as a collection of methodologies that process and classify speech signals to detect emotions embedded in them [2]. Among the most critical issues to consider in an SER system are: i) definition of the kind of emotions to classify, ii) look for suitable datasets, iii) selection of the proper input features and iv) optimisation of the convenient features. This work will consider four of the well-known dataset in the literature: EmoDB, TESS, SAVEE and RAVDSS. Thus, this study focuses on designing a low-power SER algorithm based on combining one prosodic feature with six spectral features to capture the rhythm and frequency. The proposal compares eleven low-power Classical classification Machine Learning techniques (CML), where the main novelty is optimising the two main parameters of the MFCC spectral feature through the meta-heuristic technique SA: the n mfcc and the hop length.The resulting algorithm could be deployed on low-cost embedded systems with limited computational power like a smart speaker. In addition, the proposed SER algorithm will be validated for four well-known SER datasets. The obtained models for the eleven CML techniques with the optimised MFCC features outperforms clearly (more than a 10%) the baseline models obtained with the not-optimised MFCC for the studied datasets.