TIMIT Speaker Profiling: A Comparison of Multi-task learning and Single-task learning Approaches

arXiv:2404.12077v1 Announce Type: cross
Abstract: 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 …

abstract accent age arxiv challenges classification comparison cs.ai cs.cl cs.lg cs.sd dataset deep learning deep learning techniques eess.as explore gender highlighting identification motivation multi-task learning profiling research speaker speaker identification study tasks type