Challenges and Opportunities in Assistive Audio Recognition Technologies for Deaf and Hard-of-Hearing (DHH)

Abstract

The advancements in assistive audio recognition technologies have progressed rapidly and have improved the accessibility for the Deaf and Hard-of-Hearing (DHH) community. This paper provides an extensive review of the current methods, including traditional techniques such as GMMs and HMMs, and modern deep learning-based techniques as Convolutional Neural Networks (CNN) and EfficientNet. The strengths and limitations of these methods are compared with respect to accuracy, computational efficiency, and noise robustness. Although these deep learning models have higher recognition rates, they impose significant computational requirements thereby restricting their usage in real-time and low-power devices. The review also revealed research gaps with a strong emphasis on energy-aware neural networks and their potential applications in smart environments, adaption strategies, and integration of IoT technol-ogies for practical implementation. As deep learning models grow in complexity and the required amount of labeled data increased to unsustainable level, future research will need to explore hybrid models that balance between performance gains and efficiency, to ensure that assistive audio recognition systems become increasingly reliable, usable, and generalizable in a broad range of acoustic environments.