A Review of Deep Learning for Speech Recognition and Its Application in Advanced Hearing Assistance for the Elderly

The aging global population is experiencing a rising prevalence of age-related hearing loss. Conventional hearing aids often fail in noisy environments, leading to user dissatisfaction. Recent advancements in deep learning, particularly in automatic speech recognition (ASR) and smart filter, highlight the potential for a new generation of hearing aids. This paper explores the transformative potential of evolving deep learning architectures to address the critical limitation of noise suppression. We review the progression of models specifically for auditory processing: from Deep Neural Networks (DNNs) for basic noise reduction, to Convolutional Neural Networks (CNNs) which analyse spectral features in audio spectrograms. Recurrent Neural Networks (RNNs) and sequence-to-sequence (seq2seq) models and transformer models which is a improved version of the Seq2Seq that further improved the handling of temporal speech patterns. We conclude that integrating these sophisticated models into next-generation hearing aids is essential for dramatically improving speech intelligibility in complex settings. This technological evolution promises to enhance the quality of life for the aging population by reducing hearing effort and promoting social engagement.