Cargando…
Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest
Speech is a direct and rich way of transmitting information and emotions from one point to another. In this study, we aimed to classify different emotions in speech using various audio features and machine learning models. We extracted various types of audio features such as Mel-frequency cepstral c...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662716/ https://www.ncbi.nlm.nih.gov/pubmed/37988352 http://dx.doi.org/10.1371/journal.pone.0291500 |
_version_ | 1785148591879225344 |
---|---|
author | Rezapour Mashhadi, Mohammad Mahdi Osei-Bonsu, Kofi |
author_facet | Rezapour Mashhadi, Mohammad Mahdi Osei-Bonsu, Kofi |
author_sort | Rezapour Mashhadi, Mohammad Mahdi |
collection | PubMed |
description | Speech is a direct and rich way of transmitting information and emotions from one point to another. In this study, we aimed to classify different emotions in speech using various audio features and machine learning models. We extracted various types of audio features such as Mel-frequency cepstral coefficients, chromogram, Mel-scale spectrogram, spectral contrast feature, Tonnetz representation and zero-crossing rate. We used a limited dataset of speech emotion recognition (SER) and augmented it with additional audios. In addition, In contrast to many previous studies, we combined all audio files together before conducting our analysis. We compared the performance of two models: one-dimensional convolutional neural network (conv1D) and random forest (RF), with RF-based feature selection. Our results showed that RF with feature selection achieved higher average accuracy (69%) than conv1D and had the highest precision for fear (72%) and the highest recall for calm (84%). Our study demonstrates the effectiveness of RF with feature selection for speech emotion classification using a limited dataset. We found for both algorithms, anger is misclassified mostly with happy, disgust with sad and neutral, and fear with sad. This could be due to the similarity of some acoustic features between these emotions, such as pitch, intensity, and tempo. |
format | Online Article Text |
id | pubmed-10662716 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106627162023-11-21 Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest Rezapour Mashhadi, Mohammad Mahdi Osei-Bonsu, Kofi PLoS One Research Article Speech is a direct and rich way of transmitting information and emotions from one point to another. In this study, we aimed to classify different emotions in speech using various audio features and machine learning models. We extracted various types of audio features such as Mel-frequency cepstral coefficients, chromogram, Mel-scale spectrogram, spectral contrast feature, Tonnetz representation and zero-crossing rate. We used a limited dataset of speech emotion recognition (SER) and augmented it with additional audios. In addition, In contrast to many previous studies, we combined all audio files together before conducting our analysis. We compared the performance of two models: one-dimensional convolutional neural network (conv1D) and random forest (RF), with RF-based feature selection. Our results showed that RF with feature selection achieved higher average accuracy (69%) than conv1D and had the highest precision for fear (72%) and the highest recall for calm (84%). Our study demonstrates the effectiveness of RF with feature selection for speech emotion classification using a limited dataset. We found for both algorithms, anger is misclassified mostly with happy, disgust with sad and neutral, and fear with sad. This could be due to the similarity of some acoustic features between these emotions, such as pitch, intensity, and tempo. Public Library of Science 2023-11-21 /pmc/articles/PMC10662716/ /pubmed/37988352 http://dx.doi.org/10.1371/journal.pone.0291500 Text en © 2023 Rezapour Mashhadi, Osei-Bonsu https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Rezapour Mashhadi, Mohammad Mahdi Osei-Bonsu, Kofi Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest |
title | Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest |
title_full | Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest |
title_fullStr | Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest |
title_full_unstemmed | Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest |
title_short | Speech emotion recognition using machine learning techniques: Feature extraction and comparison of convolutional neural network and random forest |
title_sort | speech emotion recognition using machine learning techniques: feature extraction and comparison of convolutional neural network and random forest |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10662716/ https://www.ncbi.nlm.nih.gov/pubmed/37988352 http://dx.doi.org/10.1371/journal.pone.0291500 |
work_keys_str_mv | AT rezapourmashhadimohammadmahdi speechemotionrecognitionusingmachinelearningtechniquesfeatureextractionandcomparisonofconvolutionalneuralnetworkandrandomforest AT oseibonsukofi speechemotionrecognitionusingmachinelearningtechniquesfeatureextractionandcomparisonofconvolutionalneuralnetworkandrandomforest |