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This article describes a modified technique for enhancing noisy speech to improve automatic speech recognition (ASR) performance. The proposed approach improves the widely used spectral subtraction which inherently suffers from the associated musical noise effects. Through a psychoacoustic masking and critical band variance normalization technique, the artifacts produced by spectral subtraction are minimized for improving the ASR accuracy. The popular advanced ETSI-2 front end is tested for comparison purposes. The performed speech recognition evaluations on the noisy standard AURORA-2 tasks show enhanced performance for all noise conditions.

Abstract. The goal of this work is to provide robust and accurate speech detection for automatic speech recognition (ASR) in meeting room settings. The solution is based on computing long-term modulation spectrum, and examining specific frequency range for dominant speech components to classify speech and non-speech signals for a given audio signal. Manually segmented speech segments, short-term energy, short-term energy and zero-crossing based segmentation techniques, and a recently proposed Multi Layer Perceptron (MLP) classifier system are tested for comparison purposes. Speech recognition evaluations of the segmentation methods are performed on a standard database and tested in conditions where the signal-to-noise ratio (SNR) varies considerably, as in the cases of close-talking headset, lapel, distant microphone array output, and distant microphone. The results reveal that the proposed method is more reliable and less sensitive to mode of signal acquisition and unforeseen conditions.