Investigation of text-independent speaker identification techniques under conditions of variable data

In this paper, we examine several methods for text-independent speaker identification and discuss the problem of achieving robust performance. In a previous paper [1], we have described a radio-channel database that contains highly variable speech data of poor quality. Here, we describe several experiments with the radio-channel database leading to the development of robust features and methods. These experiments show that robustness to one degradation may not be sufficient to improve speaker identification accuracy. A robust feature set and modeling and classification method should mitigate the effects of many of the degradations in the data.