TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches
It addresses speaker profiling for speech processing applications, but is incremental as it compares existing methods without major breakthroughs.
This study compared multi-task and single-task deep learning approaches for speaker profiling tasks like gender classification and age estimation on the TIMIT dataset, finding that multi-task learning benefits tasks of similar complexity but faces challenges in accent classification.
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.