  This study employs deep learning techniques to explore four speaker profiling
tasks on the TIMIT dataset, namely gender classification, accent
classification, age estimation, and speaker identification, highlighting the
potential and challenges of multi-task learning versus single-task models. The
motivation for this research is twofold: firstly, to empirically assess the
advantages and drawbacks of multi-task learning over single-task models in the
context of speaker profiling; secondly, to emphasize the undiminished
significance of skillful feature engineering for speaker recognition tasks. The
findings reveal challenges in accent classification, and multi-task learning is
found advantageous for tasks of similar complexity. Non-sequential features are
favored for speaker recognition, but sequential ones can serve as starting
points for complex models. The study underscores the necessity of meticulous
experimentation and parameter tuning for deep learning models.
