Artificial Intelligence for Voice-Based Age Estimation

Abstract

Voice-based age estimation is an emerging field of study with significant applications in biometric security, healthcare, and personalized services. Our work focuses on the development and evaluation of a Long Short-Term Memory (LSTM) based solution trained on the Common Voice dataset, specifically targeting English-speaking demographics across various regions. This study's primary focus is providing accurate age estimates from voice data. Our model extracts spectral features and Mel Frequency Cepstral Coefficients (MFCCs) from voice samples, taking into consideration the gender and accent of the speaker to better estimate the age. To tackle this problem, we implemented our solution using Python in the Jupyter Notebook environment, employing tools such as Keras for model creation and Librosa for sound processing. The results are very encouraging.