Whispered speaker verification and gender detection using weighted instantaneous frequencies

In this paper, automatic speaker verification and gender detection using whispered speech is explored. Whispered speech, despite its reduced perceptibility, has been shown to convey relevant speaker identity and gender information. This study compares the performance of a GMM-UBM speaker verification system trained with normal and whispered speech under different matched and mismatched conditions, and describes the benefits of adaptation in a speaking-style independent model to handle both vocal efforts. It is shown that performance improvements can be achieved by using speaking-style and gender dependent models, as well as by adding features based on the AM-FM signal representation. Moreover, the AM-FM based features showed to be more discriminative than classical MFCCs for whispered speech gender detection. Experimental results suggest that whispered speech carries sufficient information for reliable automatic speaker identification.