Robust speech detection and segmentation for real-time ASR applications

This paper provides a solution for robust speech detection that can be applied across a variety of tasks. The solution is based on an algorithm that performs non-parametric estimation of the background noise spectrum using minimum statistics of the smoothed short-time Fourier transform (STFT). It is shown that the new algorithm can operate effectively under varying signal-to-noise ratios. Results are reported on two tasks - HMIHY and SPINE - which differ in their speaking style, background noise type and bandwidth. With a computational cost of less than 2% real-time on a 1GHz P-3 machine and a latency of 400 ms, it is suitable for real-time ASR applications.