Spectral slope distance measures with linear prediction analysis for word recognition in noise

This paper discusses the approximation and use of spectral slope distance measures derived from linear prediction analysis models of speech, with emphasis on their application for recognition of noisy speech. Initial testing of these slope-based measures for speaker-dependent isolated word recognition indicates that they give considerable performance improvement over the standard cepstral distance measure in several noise conditions. Comparisons are also made to two related distance measures which have been recently reported by other researchers.