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 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.
Abstract (translated)
本研究采用深度学习技术对TIMIT数据集中的四个说话人分类任务进行研究，包括性别分类、口音分类、年龄估计和说话人识别，强调了多任务学习与单任务模型的优势和挑战。本研究的研究动机是双重的：首先，旨在通过实验检验多任务学习在说话人分类任务中相对于单任务模型的优势和劣势；其次，强调了对说话人识别任务中技能化特征工程的重要性。研究结果揭示了口音分类任务的挑战，而多任务学习在任务复杂度相似的情况下具有优势。非序列特征在说话人识别中受到青睐，但序列特征可以作为复杂模型的起点。本研究强调了对于深度学习模型的实验和参数调整的必要性。
URL
https://arxiv.org/abs/2404.12077
