A Sinusoidal Model Approach to Acoustic Landmark Detection and Segmentation for Robust Segment-Based Speech Recognition

Abstract

In this paper, we present a noise robust landmark detection and segmentation algorithm using a sinusoidal model representation of speech. We compare the performance of our approach under noisy conditions against two segmentation methods used in the SUMMIT segment-based speech recognizer, a full segmentation approach and an approach that detects segment boundaries based on spectral change. The word error rate of the spectral change segmentation method degrades rapidly in the presence of noise, while the sinusoidal and full segmentation models degrade more gracefully. However, the full segmentation method requires the largest computation time of the three approaches. We find that our new algorithm provides the best tradeoff between word accuracy and computation time of the three methods. Furthermore, we find that our model is robust when speech is contaminated by various noise types.