A New Silence Removal and Endpoint Detection Algorithm for Speech and Speaker Recognition Applications

Pre-processing of Speech Signal serves various purposes in any speech processing application. It includes Noise Removal, Endpoint Detection, Pre-emphasis, Framing, Windowing, Echo Canceling etc. Out of these, silence/unvoiced portion removal along with endpoint detection is the fundamental step for applications like Speech and Speaker Recognition. The proposed method uses Probability Density Function (PDF) of the background noise and a Linear Pattern Classifier for classification of Voiced part of a speech from silence/unvoiced part. The work shows better end point detection as well as silence removal than conventional Zero Crossing Rate (ZCR) and Short Time Energy (STE) function methods.