Exploring Speaker Profiling Tasks on the TIMIT Dataset: A Comparative Analysis of Multi-Task and Single-Task Learning Approaches

This study compares the performance of multi-task learning and single-task learning approaches in addressing four speaker profiling tasks on the TIMIT dataset: gender classification, accent classification, age estimation, and speaker identification. The findings highlight the challenges in accent classification and the advantages of multi-task learning for tasks of similar complexity.