ArticleComputational intelligence and neuroscience2022
Human-Computer Interaction with Detection of Speaker Emotions Using Convolution Neural Networks.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- From Neural Networks to Emotional Networks: A Systematic Review of EEG-Based Emotion Recognition in Cognitive Neuroscience and Real-World Applications.Brain sciences · 2025Review
- Hybrid CNN-LSTM model with efficient hyperparameter tuning for prediction of Parkinson's disease.Scientific reports · 2023Article
- A Multimodal Feature Fusion Framework for Sleep-Deprived Fatigue Detection to Prevent Accidents.Sensors (Basel, Switzerland) · 2023Article
- A Deep Learning Method Using Gender-Specific Features for Emotion Recognition.Sensors (Basel, Switzerland) · 2023Article
- Text-Based Emotion Recognition Using Deep Learning Approach.Computational intelligence and neuroscience · 2022Article
- Region-Based Segmentation and Classification for Ovarian Cancer Detection Using Convolution Neural Network.Contrast media & molecular imaging · 2022Article
- Analysis of Smart Lung Tumour Detector and Stage Classifier Using Deep Learning Techniques with Internet of Things.Computational intelligence and neuroscience · 2022Article
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Authors and funding
8 authors.
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Abstract
Emotions play an essential role in human relationships, and many real-time applications rely on interpreting the speaker's emotion from their words. Speech emotion recognition (SER) modules aid human-computer interface (HCI) applications, but they are challenging to implement because of the lack of balanced data for training and clarity about which features are sufficient for categorization. This research discusses the impact of the classification approach, identifying the most appropriate combination of features and data augmentation on speech emotion detection accuracy. Selection of the correct combination of handcrafted features with the classifier plays an integral part in reducing computation complexity. The suggested classification model, a 1D convolutional neural network (1D CNN), outperforms traditional machine learning approaches in classification. Unlike most earlier studies, which examined emotions primarily through a single language lens, our analysis looks at numerous language data sets. With the most discriminating features and data augmentation, our technique achieves 97.09%, 96.44%, and 83.33% accuracy for the BAVED, ANAD, and SAVEE data sets, respectively.
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