Spectral basis functions from discriminant analysis
Hynek Heřmanský, Narendranath Malayath · 1998
The work examines Karhunen-Loeve Transform and Linear Discriminant Analysis as means for designing optimized spectral bases for the projection of the critical-band auditory-like spectrum. 1. INTRODUCTION 1.1. The state-of-art Typical large vocabulary automatic recognition of speech (ASR) consists of three main components: feature extraction, pattern classification, and language modeling. The feature extraction attempts to reduce the information rate of raw speech data by alleviating irrelevant variability such as speaker characteristics or environmental noise, the pattern classification further reduces information rate by classifying each time instant into one of (phoneme-like) subword-unit classes, and language modeling compensates for possible errors of classification by emphasizing more likely word combinations. Over the past two decades we witnessed the introduction of stochastic approaches in both the pattern classification and the language modeling modules. Stochastic technique...