Echoes of Gender: Unveiling Voice Identity with Neural Networks

The broad applications of voice-based gender detection in fields including security systems, sociolinguistic research, and human-computer interaction have attracted a lot of attention in recent years. The objective of this work is to investigate the viability and efficiency of classifying gender using only audio data and neural network techniques. We do this by using deep learning techniques to identify gender-signalizing patterns in a neural network trained on a variety of datasets containing voices, both male and female. We show the effectiveness of our approach in reliably guessing gender from voice recordings through testing and assessment. We also address the possible uses, difficulties, and ramifications of voice-based gender recognition technology, emphasizing its importance in the modern world.