Building a Gender Detection Model using Voice Data

Training a machine learning model to determine a speaker’s gender from audio input is the first step in creating a gender detection model utilising voice data. In order to increase model robustness, the first step is to gather a labelled dataset of voice recordings from both male and female speakers, ideally with a range of accents, ages, and recording settings. Since they convert unprocessed audio into useful features that reflect attributes like pitch, tone, and frequency, audio processing techniques like extracting Mel-Frequency Cepstral Coefficients (MFCCs) are crucial. In order to classify the gender based on patterns in the audio data, algorithms like Support Vector Machines (SVM), Random Forests, or neural networks employ these features as the model’s input for training.Through repeated exposure to these characteristics, the model gains the ability to distinguish between vocal characteristics typically associated with male and female voices. Managing differences in speech patterns, background noise, and possible linguistic or cultural influences on voice characteristics are among the difficulties. The model is appropriate for use in speech-activated systems, virtual assistants, and customer service because, once trained, it can identify new voice data in batch processing or real-time.