Modelling the recognition of spectrally reduced speech
Jon Barker, Martin P. Cooke · 1997
Progress in robust automatic speech recognition may benefit from a fuller account of the mechanisms and representations used by listeners in processing distorted speech. This paper reports on a number of studies which consider how recognisers trained on clean speech can be adapted to cope with a particular form of spectral distortion, namely reduction of clean speech to sine-wave replicas. Using the Resource Management corpus, the first set of recognition experiments confirm the high information content of sine-wave replicas by demonstrating that such tokens can be recognised at levels approaching those for natural speech if matched conditions apply during training. Further recognition tests show that sine-wave speech can be recognised using natural speech models if a spectral peak representation is employed in concert with occluded speech recognition techniques. 1. INTRODUCTION Clean speech and speech with additive noise have been the primary conditions employed in most ASR studies....