MULTIMODAL SPEECH PROCESSING

Abstract

The aim of this research is to evaluate the effectiveness of multimodal speech processing techniques in improving speech recognition accuracy, addressing the key issue of integrating audio, visual, and contextual cues in real-time applications; to solve this problem, data is required from diverse speech datasets that encompass various modalities, including video input, audio recordings, and contextual language information.

This dissertation examines the effectiveness of multimodal speech processing techniques in enhancing speech recognition accuracy, focusing particularly on the integration of audio, visual, and contextual cues for real-time applications. The research identifies significant challenges in the current speech recognition systems and emphasizes the necessity of utilizing diverse speech datasets that incorporate modalities such as video input, audio recordings, and contextual language information. Key findings reveal that incorporating visual cues along with auditory signals considerably improves recognition rates, particularly in environments with ambient noise, leading to a marked reduction in misunderstanding critical messages in communication. The implications of these findings are particularly significant within the healthcare sector, where effective communication between patients and providers is essential for accurate diagnosis and treatment. Improved speech recognition capabilities can enhance telemedicine services, assistive technologies for individuals with speech impairments, and overall patient-provider interactions, ultimately leading to better health outcomes. This study not only contributes to the existing body of knowledge in speech processing but also highlights the potential for transformative applications in healthcare, encouraging future research and development that leverages multimodal approaches to enhance communication efficacy in clinical settings.