A deep learning framework for gender sensitive speech emotion recognition based on MFCC feature selection and SHAP analysis
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| Publicado en: | Scientific Reports (Nature Publisher Group) vol. 15, no. 1 (2025), p. 28569-28588 |
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 022 | |a 2045-2322 | ||
| 024 | 7 | |a 10.1038/s41598-025-14016-w |2 doi | |
| 035 | |a 3236805064 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 274855 |2 nlm | ||
| 100 | 1 | |a Hu, Qingqing |u Faculty of Humanities and Arts, Macau University of Science and Technology, Avenida Wai Long, 999078, Taipa, Macau, China (ROR: https://ror.org/03jqs2n27) (GRID: grid.259384.1) (ISNI: 0000 0000 8945 4455) | |
| 245 | 1 | |a A deep learning framework for gender sensitive speech emotion recognition based on MFCC feature selection and SHAP analysis | |
| 260 | |b Nature Publishing Group |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Speech is one of the most efficient methods of communication among humans, inspiring advancements in machine speech processing under Natural Language Processing (NLP). This field aims to enable computers to analyze, comprehend, and generate human language naturally. Speech processing, as a subset of artificial intelligence, is rapidly expanding due to its applications in emotion recognition, human-computer interaction, and sentiment analysis. This study introduces a novel algorithm for emotion recognition from speech using deep learning techniques. The proposed model achieves up to a 15% improvement compared to state-of-the-art deep learning methods in speech emotion recognition. It employs advanced supervised learning algorithms and deep neural network architectures, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) units. These models are trained on labeled datasets to accurately classify emotions such as happiness, sadness, anger, fear, surprise, and neutrality. The research highlights the system’s real-time application potential, such as analyzing audience emotional responses during live television broadcasts. By leveraging advancements in deep learning, the model achieves high accuracy in understanding and predicting emotional states, offering valuable insights into user behavior. This approach contributes to diverse domains, including media analysis, customer feedback systems, and human-machine interaction, showcasing the transformative potential of combining speech processing with neural networks. | |
| 653 | |a Physiology | ||
| 653 | |a Language | ||
| 653 | |a Accuracy | ||
| 653 | |a Happiness | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Gender | ||
| 653 | |a Feature selection | ||
| 653 | |a Speech | ||
| 653 | |a Machine translation | ||
| 653 | |a Long short-term memory | ||
| 653 | |a Emotions | ||
| 653 | |a Deep learning | ||
| 653 | |a Voice recognition | ||
| 653 | |a Neural networks | ||
| 653 | |a Support vector machines | ||
| 653 | |a Classification | ||
| 653 | |a Algorithms | ||
| 653 | |a Natural language processing | ||
| 653 | |a Information processing | ||
| 653 | |a Computers | ||
| 653 | |a Females | ||
| 653 | |a Males | ||
| 653 | |a Economic | ||
| 700 | 1 | |a Peng, Yiran |u Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, 999078, Taipa, Macau, China (ROR: https://ror.org/03jqs2n27) (GRID: grid.259384.1) (ISNI: 0000 0000 8945 4455) | |
| 700 | 1 | |a Zheng, Zhong |u Faculty of Humanities and Arts, Macau University of Science and Technology, Avenida Wai Long, 999078, Taipa, Macau, China (ROR: https://ror.org/03jqs2n27) (GRID: grid.259384.1) (ISNI: 0000 0000 8945 4455) | |
| 773 | 0 | |t Scientific Reports (Nature Publisher Group) |g vol. 15, no. 1 (2025), p. 28569-28588 | |
| 786 | 0 | |d ProQuest |t Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3236805064/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3236805064/fulltext/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3236805064/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |