HIERARCHICAL CLASSIFICATION TREE MODELING OF NONSTATIONARY NOISE FOR ROBUST SPEECH RECOGNITION

Abstract

Noise robustness is a key issue in successful deployment of automatic speech recognition systems in demanding environments such as hospital operating rooms. Perhaps the most successful way to overcome the additive noise obstacle is to employ a model adaptation scheme built around a set of dedicated clean speech and noise-only statistical models. Existing recognizer designs generally rely on relatively simple noise models, as more detailed ones would increase computational demands significantly. Simple models are, however, unable to provide accurate characterization of highly nonstationary noise present in real-world noisy facilities and thereby provide only limited reduction in error rate of the recognizer. The present article describes a novel approach to nonstationary acoustical noise modeling via a set of hierarchically tied hidden Markov models in a classification tree structure. Proposed statistical structure allows detailed description of nonstationary ambient acoustical noise while maintaining low computational costs during recognition. Modeling performance of the proposed construction is verified on a real background noise recorded during a neurosurgery in a hospital operating room.