Robust Multi-Scenario Speech-Based Emotion Recognition System
Every human being experiences emotions daily, e.g., joy, sadness, fear, anger. These might be revealed through speech—words are often accompanied by our emotional states when we talk. Different acoustic emotional databases are freely available for solving the Emotional Speech Recognition (ESR) task....
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/22/6/2343 |
| _version_ | 1797442334447632384 |
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| author | Fangfang Zhu-Zhou Roberto Gil-Pita Joaquín García-Gómez Manuel Rosa-Zurera |
| author_facet | Fangfang Zhu-Zhou Roberto Gil-Pita Joaquín García-Gómez Manuel Rosa-Zurera |
| author_sort | Fangfang Zhu-Zhou |
| collection | DOAJ |
| description | Every human being experiences emotions daily, e.g., joy, sadness, fear, anger. These might be revealed through speech—words are often accompanied by our emotional states when we talk. Different acoustic emotional databases are freely available for solving the Emotional Speech Recognition (ESR) task. Unfortunately, many of them were generated under non-real-world conditions, i.e., actors played emotions, and recorded emotions were under fictitious circumstances where noise is non-existent. Another weakness in the design of emotion recognition systems is the scarcity of enough patterns in the available databases, causing generalization problems and leading to overfitting. This paper examines how different recording environmental elements impact system performance using a simple logistic regression algorithm. Specifically, we conducted experiments simulating different scenarios, using different levels of Gaussian white noise, real-world noise, and reverberation. The results from this research show a performance deterioration in all scenarios, increasing the error probability from 25.57% to 79.13% in the worst case. Additionally, a virtual enlargement method and a robust multi-scenario speech-based emotion recognition system are proposed. Our system’s average error probability of 34.57% is comparable to the best-case scenario with 31.55%. The findings support the prediction that simulated emotional speech databases do not offer sufficient closeness to real scenarios. |
| first_indexed | 2024-03-09T12:40:20Z |
| format | Article |
| id | doaj.art-ee3b1151b9db47428fcba27149206076 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T12:40:20Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ee3b1151b9db47428fcba271492060762023-11-30T22:19:44ZengMDPI AGSensors1424-82202022-03-01226234310.3390/s22062343Robust Multi-Scenario Speech-Based Emotion Recognition SystemFangfang Zhu-Zhou0Roberto Gil-Pita1Joaquín García-Gómez2Manuel Rosa-Zurera3Department of Signal Theory and Communications, University of Alcalá, 28805 Alcalá de Henares, Madrid, SpainDepartment of Signal Theory and Communications, University of Alcalá, 28805 Alcalá de Henares, Madrid, SpainDepartment of Signal Theory and Communications, University of Alcalá, 28805 Alcalá de Henares, Madrid, SpainDepartment of Signal Theory and Communications, University of Alcalá, 28805 Alcalá de Henares, Madrid, SpainEvery human being experiences emotions daily, e.g., joy, sadness, fear, anger. These might be revealed through speech—words are often accompanied by our emotional states when we talk. Different acoustic emotional databases are freely available for solving the Emotional Speech Recognition (ESR) task. Unfortunately, many of them were generated under non-real-world conditions, i.e., actors played emotions, and recorded emotions were under fictitious circumstances where noise is non-existent. Another weakness in the design of emotion recognition systems is the scarcity of enough patterns in the available databases, causing generalization problems and leading to overfitting. This paper examines how different recording environmental elements impact system performance using a simple logistic regression algorithm. Specifically, we conducted experiments simulating different scenarios, using different levels of Gaussian white noise, real-world noise, and reverberation. The results from this research show a performance deterioration in all scenarios, increasing the error probability from 25.57% to 79.13% in the worst case. Additionally, a virtual enlargement method and a robust multi-scenario speech-based emotion recognition system are proposed. Our system’s average error probability of 34.57% is comparable to the best-case scenario with 31.55%. The findings support the prediction that simulated emotional speech databases do not offer sufficient closeness to real scenarios.https://www.mdpi.com/1424-8220/22/6/2343affective computingemotion recognitionspeech emotions |
| spellingShingle | Fangfang Zhu-Zhou Roberto Gil-Pita Joaquín García-Gómez Manuel Rosa-Zurera Robust Multi-Scenario Speech-Based Emotion Recognition System Sensors affective computing emotion recognition speech emotions |
| title | Robust Multi-Scenario Speech-Based Emotion Recognition System |
| title_full | Robust Multi-Scenario Speech-Based Emotion Recognition System |
| title_fullStr | Robust Multi-Scenario Speech-Based Emotion Recognition System |
| title_full_unstemmed | Robust Multi-Scenario Speech-Based Emotion Recognition System |
| title_short | Robust Multi-Scenario Speech-Based Emotion Recognition System |
| title_sort | robust multi scenario speech based emotion recognition system |
| topic | affective computing emotion recognition speech emotions |
| url | https://www.mdpi.com/1424-8220/22/6/2343 |
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