• Dr. Valli Madhavi Koti
• Meduri Sridhar Sarma
• Dr. K Jaya Sudha

QThis research investigates the integration of multi-modal information, specifically images and speech, to enhance the recognition capabilities of artificial intelligence (AI) systems. Adopting an interpretive philosophy and employing a deductive approach, the study explores the potential of dynamic attention mechanisms, semi-supervised learning, and cross-domain adaptation techniques. A descriptive research design is employed, utilizing secondary data collection from reputable academic sources. The research critically evaluates the feasibility and applicability of hardware optimization for efficient multi-modal processing, considering factors like specialized processors and parallel computing. The study presents a thorough analysis of dynamic attention mechanisms, emphasizing their role in dynamically allocating attention across different modalities based on contextual relevance. Additionally, it delves into semi-supervised learning techniques, showcasing their ability to leverage both labeled and unlabeled data for improved recognition performance. Cross-domain adaptation techniques are explored to facilitate the seamless deployment of multi-modal fusion models in diverse real-world scenarios.

AI systems, knowledge, connecting, integrating, multi-modal classification, aural, visual information

"MULTI-MODAL FUSION FOR ENHANCED IMAGE AND SPEECH RECOGNITION IN AI SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 12, page no.375-384, December-2023, Available :http://www.jetir.org/papers/JETIRGA06042.pdf

"MULTI-MODAL FUSION FOR ENHANCED IMAGE AND SPEECH RECOGNITION IN AI SYSTEMS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 12, page no. pp375-384, December-2023, Available at : http://www.jetir.org/papers/JETIRGA06042.pdf