Speech recognition based on the subspace method : AI class-description learning viewpoint

概要

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This paper describes the learning mechanism employed in a highly efficient user-adaptive speech recognizer based on the subspace method for large vocabulary Japanese test input. Comparing the subspace -based learning system with the well-known AI learning system ARCH, the following points are made:(1)Subspace learning using covariance matrix modification and KL-expansion is a kind of class-description learning from examples, as found in ARCH. The subspace learning method focusses on feature extraction, which results in a powerful representation of pattern characteristics for each pattern class, but does not involve only pattern classification, unlike conventional pattern recognition methods. (2)The concepts of ・Near-Miss, " ・Require-Link" and ・Forbid-Link" in ARCH can be simulated with the subspace method. Since the subspace method deals with patterns but not symbols, it does not need pattern-symbol conversion. In other words, the subspace learning method has a more versatile description capability than ARCH. (3)Minsky's concept of ・Uniframe" is implemented in a speech recognizer based on the subspace method. The ・Uniframe" obtained with KL-expansion is equivalent to subspace which represents a meaning of a class. Minsky's ・Accumulation" and ・Exceptional Principal" concepts have also been taken into account.
社団法人日本音響学会の論文