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.

翻译：本研究采用深度学习技术，在TIMIT数据集上探索了四项说话人特征分析任务，即性别分类、口音分类、年龄估计和说话人识别，揭示了多任务学习与单任务模型相比的潜力与挑战。研究动机有两点：首先，实证评估多任务学习相比单任务模型在说话人特征分析中的优势与不足；其次，强调熟练的特征工程对说话人识别任务持续不变的重要性。研究结果显示了口音分类的挑战，并发现多任务学习对于复杂度相似的任务具有优势。非序列特征更受说话人识别任务青睐，而序列特征可作为复杂模型的起点。本研究强调了深度学习模型必须进行细致的实验与参数调优。