A robust endpoint detection of speech for noisy environments with application to automatic speech recognition

We propose a new approach for classifying speech vs. non-speech, which proves to significantly improve speech recognition performance under noise. The proposed algorithm relies on the energy and spectral characteristics of the signal and applies a 3-level two-dimensional thresholds to determine whether an input frame in speech or non-speech. The algorithm runs in real-time, and offers better immunity to background noise, and to background speech than traditional energy-based word boundary detection. The performance of the endpoint detector is reported here in terms of improvements in speaker-independent (SI) and speaker-dependent (SD) recognition performance using 5 different simulated noise conditions and various signal-to-noise ratios (SNR). The proposed endpoint detection of speech improves the SD recognition accuracy by 24% for office noise, and reduces the false rejection rates for both SI and SD by 45% for babble noise and lobby noise.