Mel-Spectrograms Based LSTM Model for Speech Emotion Recognition

Emotion recognition from audio data holds immense potential in revolutionizing human-computer interaction (HMI), affective computing, and psychological health monitoring. This paper delves into a novel deep learning approach that leverages the strengths of multimodal features mined from audio signals. We propose a model that transcends the disadvantages of existing methods by combining Mel-Frequency Cepstral Coefficients (MFCCs) with high-level representations extracted from a pre-trained DenseNet architecture. MFCCs provide a compressed representation of the audio signal's spectral characteristics, capturing crucial emotional cues like pitch and intensity. These learned patterns can translate to the domain of audio emotion recognition, enabling the model to identify subtle emotional nuances that might be difficult to capture with traditional feature engineering techniques. Our deep learning model, comprised of dense layers, fosters robust performance in accurately classifying emotions across diverse categories. We used a Mel-spectrograms-based LSTM model for speech emotion recognition that effectively identifies various emotions. We rigorously evaluate the proposed approach on the TESS dataset. The experimental results are truly compelling, showcasing a staggering accuracy of 100%. This exceptional performance signifies the effectiveness of the multimodal approach in extracting and interpreting emotional cues from audio data.