Analysis of Distance Measures for Pre-Quantization before Feature Extraction in Automatic Speaker Recognition

The total recognition time as well as the memory requirement in speaker recognition is mainly governed by the number of speakers, the number of frame vectors in the test sequence and the feature dimensionality. The adjacent frame vectors can show similarity in the feature space because of the slow movements of the articulators. Hence efficient frame selection techniques to select non-redundant frames in the preprocessing stage will be very effective in real time application of this recognition system. In pre-quantization (PQ) we select a new sequence of frames Y from the original frames X such that length of Y is less than X. In this paper we propose different distance measure techniques for selecting frames exploiting the redundancies between consecutive frames. The aim is not only to reduce the number of frames for feature extraction but also to maintain the recognition accuracy reasonably high by selecting suitable frames containing speaker specific information. The techniques are evaluated on two different telephone speech databases, POLYCOST and KING.