Paper Key : IRJ************776
Author: Sumsuddin Shaik
Date Published: 20 Nov 2024
Abstract
Speech processing is a fundamental area of artificial intelligence with applications ranging from voice assistants and transcription services to emotion detection and communication aids. Traditional unimodal approachesrelying solely on audio signalshave made significant strides in improving recognition rates. However, they often fail in real-world environments characterized by noise, speaker variability, or contextual ambiguities. Multi-modal machine learning techniques integrate diverse data sources such as audio, visual cues (e.g., lip movements and facial expressions), and text to overcome these limitations. By combining complementary modalities, these methods deliver enhanced performance in terms of robustness, accuracy, and contextual understanding. This review provides a comprehensive analysis of multi-modal machine learning techniques for speech processing, focusing on their design, methodologies, applications, and challenges. We also explore future directions, including lightweight architectures and advanced fusion strategies, to facilitate real-time deployment and scalability in practical applications.