Speaker height estimation combining GMM and linear regression subsystems

There are both scientific and technology based motivations for establishing effective speech processing algorithms that estimate speaker traits. Estimating speaker height can assist in voice forensic analysis [1], as well as provide additional side knowledge to improve speaker ID systems, or acoustic model selection for improved speech recognition. In this study, two distinct approaches for height estimation are explored. The first approach is statistical based and incorporates acoustic models within a GMM structure, while the second is a direct speech analysis approach that employs linear regression to obtain the height directly. The accuracy and trade-offs of these systems are explored as well a fusion of the two systems using data from the TIMIT corpus (which includes ground truth on speaker height).