Automatic Speaker Profiling (ASP), is concerned with estimating the physical traits of a person from their voice. These traits include gender, age, ethnicity, and physical parameters. Reliable ASP has a wide range of applications such as mobile shopping, customer service, robotics, forensics, security, and surveillance systems. Research in ASP has gained interest in the last decade, however, it was focused on different tasks individually, such as age, height, or gender. In this work, a review of existing studies on different tasks of speaker profiling is performed. These tasks include age estimation and classification, gender detection, height, and weight estimation This study aims to provide insight into the work of ASP, available datasets, feature extraction techniques, and learning models. Further, the performance of current speaker profiling systems is investigated. Finally, the challenges of speaker profiling are presented at the end of this review.
