###### Abstract  
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. t 2024 14 April 2024 25 May 2024 29 May 2024 29 May 2024  
Education is among the many fields in which SER can be applied, with emotions profoundly affecting student engagement and motivation, thereby impacting learning outcomes [3]. Several studies have shown that SER can be used to obtain important information about how students feel, especially in a distance learning context, and these results can inform adaptive teaching strategies. For example, if a student's speech signals indicate confusion or frustration, SER can prompt the educator to provide supplementary explanations or personalized aid [4]. Meanwhile, other studies have investigated how instructors' emotional stability affects how effectively they deliver information and the effect that this has on student engagement and information, which can be used to improve teaching quality and student outcomes [5]. The field of deep learning (DL) has made significant progress in revolutionizing the ability of machines to comprehend and interpret human emotions. DL models can currently analyze the intricate characteristics of unprocessed data, including voice recordings, to detect emotional cues. Emotional recognition technology has the potential to enhance human-machine interactions in a seamless and instinctive manner across a wide range of applications, and swift advancements in this domain underscore DL's profound capacity to facilitate machines' recognition of and reactions to human emotions. Accordingly, the model proposed herein can significantly advance the field of SER by equipping machines with a human-like ability to discern emotions such as happiness, sadness, and anger in human speech [6]. By enabling machines to analyze subtle variations in tone, cadence, and timbre, DL allows the deduction of emotional states from voice signals.