Speech Based Age

Speech-based age estimation research aims to accurately determine a speaker's age from their voice, focusing on overcoming challenges posed by individual vocal variations and aging effects. Current research employs various deep learning architectures, including transformer-based models and convolutional neural networks, often incorporating techniques like mutual information minimization to disentangle age and speaker identity. This field is significant for applications in forensic science, healthcare (e.g., diagnosing developmental disorders), and personalized technology, with ongoing efforts to improve accuracy and robustness across diverse populations and speaking conditions.