Enhancing Speech Emotion Recognition using Deep Learning Networks on Live Calls

In the realm of virtual assistants, mental health assessments, and customer service, incorporating emotion detection in speech is essential for effective communication between humans and machines. A variety of distinct speech characteristics can be leveraged to extract valuable insights from audio samples. Our objective is to develop an emotion identification system that utilizes these detected attributes within the audio samples. In the field of machine learning, there exist traditional models such as Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) classifiers that can be applied to Speech Emotion Recognition (SER) systems. Our proposed model consists of a 1D CNN-LSTM architecture, with four Local Feature Learning Blocks (LFLBs) followed by a primary LSTM layer. Each LFLB comprises a single convolutional layer and a single max-pooling layer, which are effective at capturing inherent correlations and constructing hierarchical relationships. The LSTM layer then processes these long-term relationships derived from the localized cognitive parameters. The combination of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) has the potential to surpass the constraints of individual networks. This study evaluates their performance using the Berlin EmoDB dataset. Our proposed method outperforms current conventional models, setting a new benchmark for accuracy and effectiveness in Speech Emotion Recognition (SER) systems.