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.

本研究采用深度学习技术在 TIMIT 数据集上探索了四个说话人特征分析任务，分别为性别分类、口音分类、年龄估计和说话人识别，突出了多任务学习与单任务模型之间的潜力和挑战。该研究旨在经验性地评估在说话人特征分析领域中，多任务学习相对于单任务模型的优势和缺点，并强调技巧性特征工程在说话人识别任务中的重要性。研究发现了口音分类方面的挑战，并且发现多任务学习对于任务的复杂性较高。在说话人识别中，非顺序特征受到偏好，而顺序特征可以作为复杂模型的起点。该研究强调了对于深度学习模型的细致实验和参数调整的必要性。