Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques
Abstract Emotion plays a dominant role in speech. The same utterance with different emotions can lead to a completely different meaning. The ability to perform various of emotion during speaking is also one of the typical characters of human. In this case, technology trends to develop advanced speec...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2023-05-01
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| Series: | EURASIP Journal on Audio, Speech, and Music Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13636-023-00290-x |
| _version_ | 1797822757056020480 |
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| author | Tong Liu Xiaochen Yuan |
| author_facet | Tong Liu Xiaochen Yuan |
| author_sort | Tong Liu |
| collection | DOAJ |
| description | Abstract Emotion plays a dominant role in speech. The same utterance with different emotions can lead to a completely different meaning. The ability to perform various of emotion during speaking is also one of the typical characters of human. In this case, technology trends to develop advanced speech emotion classification algorithms in the demand of enhancing the interaction between computer and human beings. This paper proposes a speech emotion classification approach based on the paralinguistic and spectral features extraction. The Mel-frequency cepstral coefficients (MFCC) are extracted as spectral feature, and openSMILE is employed to extract the paralinguistic feature. The machine learning techniques multi-layer perceptron classifier and support vector machines are respectively applied into the extracted features for the classification of the speech emotions. We have conducted experiments on the Berlin database to evaluate the performance of the proposed approach. Experimental results show that the proposed approach achieves satisfied performances. Comparisons are conducted in clean condition and noisy condition respectively, and the results indicate better performance of the proposed scheme. |
| first_indexed | 2024-03-13T10:13:51Z |
| format | Article |
| id | doaj.art-b258064cd13a4a2b81500ec271f82a0b |
| institution | Directory Open Access Journal |
| issn | 1687-4722 |
| language | English |
| last_indexed | 2024-03-13T10:13:51Z |
| publishDate | 2023-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EURASIP Journal on Audio, Speech, and Music Processing |
| spelling | doaj.art-b258064cd13a4a2b81500ec271f82a0b2023-05-21T11:22:37ZengSpringerOpenEURASIP Journal on Audio, Speech, and Music Processing1687-47222023-05-012023111410.1186/s13636-023-00290-xParalinguistic and spectral feature extraction for speech emotion classification using machine learning techniquesTong Liu0Xiaochen Yuan1Faculty of Applied Sciences, Macao Polytechnic UniversityFaculty of Applied Sciences, Macao Polytechnic UniversityAbstract Emotion plays a dominant role in speech. The same utterance with different emotions can lead to a completely different meaning. The ability to perform various of emotion during speaking is also one of the typical characters of human. In this case, technology trends to develop advanced speech emotion classification algorithms in the demand of enhancing the interaction between computer and human beings. This paper proposes a speech emotion classification approach based on the paralinguistic and spectral features extraction. The Mel-frequency cepstral coefficients (MFCC) are extracted as spectral feature, and openSMILE is employed to extract the paralinguistic feature. The machine learning techniques multi-layer perceptron classifier and support vector machines are respectively applied into the extracted features for the classification of the speech emotions. We have conducted experiments on the Berlin database to evaluate the performance of the proposed approach. Experimental results show that the proposed approach achieves satisfied performances. Comparisons are conducted in clean condition and noisy condition respectively, and the results indicate better performance of the proposed scheme.https://doi.org/10.1186/s13636-023-00290-xSpeech emotion classificationParalinguistic featuresSpectral featuresSupport vector machineMulti-layer perceptron classifier |
| spellingShingle | Tong Liu Xiaochen Yuan Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques EURASIP Journal on Audio, Speech, and Music Processing Speech emotion classification Paralinguistic features Spectral features Support vector machine Multi-layer perceptron classifier |
| title | Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques |
| title_full | Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques |
| title_fullStr | Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques |
| title_full_unstemmed | Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques |
| title_short | Paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques |
| title_sort | paralinguistic and spectral feature extraction for speech emotion classification using machine learning techniques |
| topic | Speech emotion classification Paralinguistic features Spectral features Support vector machine Multi-layer perceptron classifier |
| url | https://doi.org/10.1186/s13636-023-00290-x |
| work_keys_str_mv | AT tongliu paralinguisticandspectralfeatureextractionforspeechemotionclassificationusingmachinelearningtechniques AT xiaochenyuan paralinguisticandspectralfeatureextractionforspeechemotionclassificationusingmachinelearningtechniques |