Efficient pre-quantization techniques based on probability density for speaker recognition system

The amount of speaker specific information in speech signal varies from frame to frame depending on spoken text and environmental conditions. A frame selection at the preprocessing stage can be an added advantage in this context. 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 first analyze a number of distance measure techniques for frame selection to exploit the redundancies of consecutive frames. Then we propose efficient techniques based on probability density function (PDF) that not only reduces the number of frames before feature extraction but also increases the recognition accuracy. The proposed methods are evaluated on two different databases, POLYCOST (telephone speech) and YOHO (microphone speech), and is shown to provide significant improvement in performance for speaker recognition.