SoundScope: Human Voice Analysis for Gender Classification

Identifying gender from voice has become an important step toward improving personalized user experiences, especially in applications such as virtual assistants, customer service systems, and voice-based authentication. Traditional manual voice pattern analysis is slow, inconsistent, and unworkable for real-world time usage. This work proposes a voice-based automatic gender classification system, one that uses machine learning techniques to identify whether a given speech sample originates from a male or female speaker. The study initially used some simple learning approaches, but they were not suitable for reliable decision-making due to their low performance. A more robust model was necessary; thus, it was trained on a large set with diverse characteristics in terms of voice samples. The resulting system instantly predicts the gender while providing probability scores, reflecting confidence in the model. The user-friendly web application was built that allowed users either to upload audio files or to record voice directly for immediate analysis. The proposed system proved efficient, practical, and well-suited for real-time voice-based classification tasks.