Speech emotion recognition based on multi-feature speed rate and LSTM

Correctly recognizing speech emotions is of significant importance in various fields, such as healthcare and human-computer interaction (HCI). However, the complexity of speech signal features poses challenges for speech emotion recognition. This study introduces a novel multi-feature method for speech emotion recognition that combines short-and rhythmic features. Utilizing short-time energy, zero-crossing rate, and average amplitude difference, the proposed approach effectively addressed overfitting concerns by reducing feature dimensionality. Employing an (LSTM) network, the experiment achieved notable accuracy across diverse datasets. Specifically, the proposed method achieved an impressive accuracy of up to 98.47% on the CASIA dataset, 100% on the Emo-DB dataset, and 98.87% on the EMOVO dataset, demonstrating its capability to accurately discern speaker emotions across different languages and emotion classes. These findings underscore the significance of incorporating speech rate for emotional content recognition, which holds promise for application in HCI and auxiliary medical diagnostics.