Speech emotion recognition with deep convolutional neural networks

By Dias Issa, M. Fatih Demirci, A. Yazıcı. Biomedical Signal Processing and Control, 2020

554 citations recorded by the source corpus.

Abstract The speech emotion recognition (or, classification) is one of the most challenging topics in data science. In this work, we introduce a new architecture, which extracts mel-frequency cepstral coefficients, chromagram, mel-scale spectrogram, Tonnetz representation, and spectral contrast features from sound files and uses them as inputs for the one-dimensional Convolutional Neural Network for the identification of emotions using samples from the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS), Berlin (EMO-DB), and Interactive Emotional Dyadic Motion Capture (IEMOCAP) datasets. We utilize an incremental method for modifying our initial model in order to improve classification accuracy. All of the proposed models work directly with raw sound data without the need for conversion to visual representations, unlike some previous approaches. Based on experimental results, our best-performing model outperforms existing frameworks for RAVDESS and IEMOCAP, thus setting the new state-of-the-art. For the EMO-DB dataset, it outperforms all previous works except one but compares favorably with that one in terms of generality, simplicity, and applicability. Specifically, the proposed framework obtains 71.61% for RAVDESS with 8 classes, 86.1% for EMO-DB with 535 samples in 7 classes, 95.71% for EMO-DB with 520 samples in 7 classes, and 64.3% for IEMOCAP with 4 classes in speaker-independent audio classification tasks.

Subfields: Experimental and Cognitive Psychology, Signal Processing

View Paper: https://www.semanticscholar.org/paper/f0354c09656fb20736a83205b19f0b6ad556774e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Experimental and Cognitive Psychology

  1. Leonardo Pepino
  2. Victoria Blanes-Vidal
  3. Luciana Ferrer
  4. Esmaeil S. Nadimi
  5. U. Rajendra Acharya
  6. Pablo Ernesto Riera
  7. Smith K. Khare

In Signal Processing

  1. Leonardo Pepino
  2. Luciana Ferrer
  3. Pablo Ernesto Riera

Citations in the corpus