Eman Abdulrahman Alkhamali

Federated learning for speech emotion recognition faces fundamental challengesin simultaneously achieving high performance, privacy preservation, and modelinterpretability. This paper introduces FedSER-XAI, a novel framework thatintegrates Particle Swarm Optimization (PSO)-based feature selection, multi-stream cross-attention mechanisms, and graph-based feature extraction withinan explainable federated learning architecture. Our approach combines VisionTransformer processing of mel-spectrograms with temporal-spatial graph convo-lutional networks to capture both contextual and structural speech relationships.The PSO algorithm achieves 78.1% dimensionality reduction (228→50 features)while improving discriminative power. The multi-stream architecture processestraditional acoustic features alongside novel graph-based representations derivedfrom visibility and correlation graphs, fused through Transformer-based cross-attention mechanisms. Extensive evaluation on EMODB and SAVEE datasetsdemonstrates exceptional performance: 99.9% and 97.2% accuracy in central-ized settings, with remarkable federated performance of 99.7% and 97.2% using8 emotion-specialized clients. The framework achieves rapid convergence within110 communication rounds, representing minimal performance degradation (0.2%for EMODB) while preserving privacy. Cross-dataset evaluation on CREMA-Dyields 68% accuracy, demonstrating reasonable generalization. The compre-hensive explainability framework using SHAP and LIME provides global andlocal interpretations, validating that graph-based features contribute significantlyto emotion discrimination. FedSER-XAI represents the first explainable feder-ated speech emotion recognition system, advancing trustworthy AI for sensitivehealthcare and human-computer interaction applications

Speech emotion recognition (SER) is a technology that can be applied to distance education to analyze speech patterns and evaluate speakers’ emotional states in real time. It provides valuable insights and can be used to enhance students’ learning experiences by enabling the assessment of their instructors’ emotional stability, a factor that significantly impacts the effectiveness of information delivery. Students demonstrate different engagement levels during learning activities, and assessing this engagement is important for controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is their instructors’ emotional state. Accordingly, this study used deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entailed integrating transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance the SER. Feature extraction from audio data used Mel-frequency cepstral coefficients; chroma; a Mel spectrogram; the zero-crossing rate; spectral contrast, centroid, bandwidth, and roll-off; and the root-mean square, with subsequent optimization processes such as adding noise, conducting time stretching, and shifting the audio data. Several transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The preprocessing and data augmentation methodologies significantly enhanced the precision of the results, with accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% for the Ryerson Audio–Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio–Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets, respectively. Furthermore, it achieved 83% accuracy on another dataset created for this study, the Saudi Higher-Education Instructor Emotions dataset. The results demonstrate the considerable accuracy of this model in detecting emotions in speech data across different languages and datasets.

Speech emotion recognition (SER) is a technology that can be applied in distance education to analyze speech patterns and evaluate speakers’ emotional states in real-time. It provides valuable insights and can be used to enhance the learning experience by enabling the assessment of instructors’ emotional stability, a factor that significantly impacts information delivery effectiveness. Students demonstrate different engagement levels during learning activities, and assessing this engagement is an important aspect of controlling the learning process and improving e-learning systems. An important aspect that may influence student engagement is the emotional states of their instructors. Accordingly, this research uses deep learning techniques to create an automated system for recognizing instructors’ emotions in their speech when delivering distance learning. This methodology entails integrating Transformer, convolutional neural network, and long short-term memory architectures into an ensemble to enhance SER. Feature extraction from audio data used Mel-frequency cepstral coefficients, chroma, Mel spectrogram, zero crossing rate, spectral contrast, centroid, bandwidth, roll-off, and root-mean square, with subsequent optimization processes adding noise to, conducting time stretching, and shifting the audio data. Notably, several Transformer blocks were incorporated, and a multi-head self-attention mechanism was employed to identify the relationships between the input sequence segments. The pre-processing and data augmentation methodologies significantly enhanced the precision of the results in that the model achieved accuracy rates of 96.3%, 99.86%, 96.5%, and 85.3% on the Ryerson Audio-Visual Database of Emotional Speech and Song, Berlin Database of Emotional Speech, Surrey Audio-Visual Expressed Emotion, and Interactive Emotional Dyadic Motion Capture datasets. Furthermore, it achieved 83% accuracy on another dataset created for this research—the Saudi Higher Education Instructor Emotions dataset. The results demonstrate this model’s considerable accuracy in detecting emotions in speech data across different languages and datasets.