Speech Emotion Recognition (SER) plays a significant role in human-computer interaction by enabling intelligent systems to recognize and respond to human emotional states. Accurate emotion recognition from speech signals is essential for applications such as mental health assessment, healthcare monitoring, virtual assistants, and human-robot interaction. However, traditional SER approaches often rely on manually engineered acoustic features and suffer from limited generalization due to dataset imbalance, speaker variability, and environmental noise. To address these challenges, this study proposes a Conv1D-based deep learning framework for robust speech emotion recognition using complementary acoustic features and data augmentation techniques. The proposed framework utilizes Mel-Frequency Cepstral Coefficients (MFCCs), Zero-Crossing Rate (ZCR), and Root Mean Square Energy (RMSE) extracted from frame-level speech signals to capture spectral, temporal, and energy-related emotional characteristics. The extracted acoustic descriptors are sequentially concatenated into a unified feature representation and provided as input to the Conv1D architecture for hierarchical temporal feature learning. Experiments were conducted using a combined multi-dataset setup consisting of RAVDESS, SAVEE, CREMA-D, and TESS datasets. The dataset was divided into training, validation, and testing subsets, and data augmentation techniques including noise injection, time shifting, pitch modification, and time stretching were applied exclusively to the training samples to improve robustness and class balance. Experimental evaluation across seven emotional categories demonstrates that the proposed framework achieves a test accuracy of 94.91% with a Macro F1-score of 0.94, showing strong recognition capability across diverse emotional speech conditions. The results indicate that the integration of discriminative acoustic feature extraction, sequential feature representation, and Conv1D-based temporal learning significantly improves speech emotion recognition performance. The proposed framework provides an effective and computationally efficient solution suitable for practical real-world SER applications.
Chourasia et al. (Wed,) studied this question.