SPEECH EMOTION RECOGNITION USING AUDIO EMBEDDINGS+WAV2VEC2.0

Speech Emotion Recognition (SER) using audio embeddings combined with the Wav2Vec2 model provides an effective approach for identifying emotions from speech signals. In this work, raw speech data is first preprocessed through noise removal, silence trimming, normalization, and resampling to improve audio quality. The processed speech is then converted into meaningful numerical representations known as audio embeddings. These embeddings capture important characteristics of speech such as pitch, tone, rhythm, and intensity.   The Wav2Vec2 model plays a crucial role in extracting deep and contextual speech features directly from raw audio signals. By leveraging self-supervised learning, Wav2Vec2 can learn powerful speech representations without requiring extensive labeled data. These learned features help improve the model’s ability to detect subtle emotional patterns in speech.        Overall, the proposed system demonstrates that deep learning–based speech representations significantly enhance emotion recognition performance. This approach can be applied in several real-world applications including human–computer interaction, mental health monitoring, call center analytics, virtual assistants, and emotion-aware systems. Future work can focus on improving model accuracy by using larger datasets, multimodal inputs such as facial expressions, and advanced deep learning architectures.

1. Surabhi V, Saurabh M. Speech emotion recognition: A review. International Research Journal of Engineering and Technology (IRJET). 2016;03:313-316  2. Nicholson, J., Takahashi, K. &Nakatsu, R. Emotion Recognition in Speech Using Neural Networks. NCA 9, 290– 296(2000). https://doi.org/10.1007/s005210070006. 3. ‘Han, Kun / Yu, Dong / Tashev, Ivan (2014): “Speech emotion recognition using deep neural network ” 4. A. Rajasekhar, M. K. Hota, ―A Study of Speech, Speaker and Emotion Recognition using Mel Frequency Cepstrum Coefficients and Support Vector Machines‖, International Conference on Communication and Signal Processing, pp. 0114-0118, 2018. 5. Zheng, W. L., Zhu, J., Peng, Y.: EEG-based emotion classification using deep belief networks. In: IEEE International Conference on Multimedia & Expo, pp. 1-6 (2014). 6. Parthasarathy S, TashevI.Convolutional neural network techniques for speech emotion recognition. In: 2018 16th International Workshop on Acoustic Signal Enhancement (IWAENC). IEEE 2018. pp. 121-125. 7. Matilda S. Emotion recognition: A survey. International Journal of Advanced Computer Research. 2015;3(1):14-19  8. Lim W, Jang D, Lee T. Speech emotion recognition using convolutional and recurrent neural networks. Asia- Pacific. 2017:1-4 9. G. Liu, W. He, B. Jin, ―Feature fusion of speech emotion recognition based on deep Learning‖, 2018 International Conference on Network Infrastructure and Digital Content (IC-NIDC), pp. 193-197, 2018.