FRAME SELECTION FOR SPEAKER IDENTIFICATION
A novel approach to automatic speaker identification is presented. Using low-level acoustic feature sets, a frame based analysis of the system performance is implemented to locate the areas of a speech signal that contain a high level of speaker dependent information. Subgroups of frames are used for the text independent speaker identification task and the resulting system performance is compared with that of using all available frames. It is found that by exclusively using the frames in the transient areas of a speech signal, where the signal shifts between being voiced and unvoiced, the rate of correctly classified frames is increased by up to 14% compared to the case of using randomly selected frames. These results are obtained for PLPCC feature sets extracted from clean speech.