New Method for Finding Optimum Number of Characteristics to Classify Speakers by Age

It is known that the amount of characteristics may be the bottleneck of a digital processing system. Finding a good method to detect which characteristics are the most important to identify a speaker would get better results with less characteristics. The classification of an adult speaker by their age is a big challenge since the adulthood is a long period without significant changes in voice. This study proposes a new method based on F-ratio, dispersion metric and also correlation between parameters to find a rank of features. A bootstrapping procedure determines the optimum number of characteristics within a feature vector to characterize a speaker. The results are compared with other non linear ranking methods. The proposed algorithm achieves a better performance in most cases.