Smart Listening: Machine Learning for Vocal Emotion Understanding

Computer Engineering

In this work, we introduce Smart Listening, a novel machine learning framework with the goal of attaining highly accurate and robust vocal emotion recognition. Emotion recognition from speech is a difficult task because there is variability in human vocal expression and noise in the environment. To overcome these difficulties, our framework is based on state-of-the-art acoustic feature extraction that takes raw speech signals and converts them into insightful spectrogram representations. The essence of the suggested system combines a Convolutional Neural Network (CNN) for feature extraction of rich spatial representations from spectrograms and a Recurrent Neural Network (RNN) for capturing sequential and temporal relationships inherent in speech. This synergy allows the system to capture local acoustic patterns and longer-term dependencies essential in emotion discrimination. In order to promote the model's generalizability and robustness, we use transfer learning, building on pre-trained acoustic models, and data augmentation methods like time stretching and noise injection to increase the size of the training set. Comprehensive experiments on the EmoDB benchmark dataset, which is popularly used in speech emotion recognition, demonstrate Smart Listening to produce a state-of-the-art accuracy of 95.6%, significantly outperforming current approaches. The suggested framework has immense potential for real-world application in emotionally conscious virtual assistants, mental wellness monitoring systems, and computerized customer support systems. By facilitating machines to better understand human emotions, this contribution marks a stepping stone towards developing compassionate and intelligent human-computer relationships

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