VOCAL MOOD DETECTION USING NATURAL LANGUAGE PROCESSING

ABSTRACT Vocal Mood Detector, a branch of Natural Language Processing (NLP), focuses on identifying emotions from spoken language using linguistic and acoustic features such as lexical content, pitch, tone, rhythm, and prosody. This process integrates advanced NLP techniques and speech signal processing to decode emotions like happiness, anger, sadness, and neutrality. Recent advancements leverage deep learning models, including recurrent and convolutional neural networks (RNNs and CNNs) and transformer-based architectures, to improve accuracy. These models capture temporal and semantic nuances, while multimodal approaches enhance performance by combining textual and acoustic data. Applications span customer service, virtual assistants, mental health monitoring, and adaptive learning systems. However, Additionally, detecting subtle emotional shifts and managing multi-speaker dialogues add complexity. This study reviews speech emotion analyzers' methodologies, tools, and applications, highlighting current limitations and future research directions. Enhanced systems promise to transform human-computer interaction by enabling more empathetic and adaptive AI.