Abstract
The application of deep learning in identifying sound features has become increasingly prevalent, greatly enhancing the performance of voice applications across various professional domains. This study focuses on deep learning techniques applied to sound feature analysis and gender recognition. It reviews methodologies, datasets, and case studies, emphasizing deep learning's crucial role in boosting efficiency and accuracy. Recent advancements highlight CNN-based architectures and novel models, demonstrating deep learning for voice systems for enhanced interaction and analysis. Challenges such as computational demands and limited data availability persist, but ongoing optimizations and multi-modal approaches promise future advancements in voice technology, enabling more intelligent and responsive interactions.
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