抄録
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 a subspace which represents a meaning of a class. Minsky's “Accumula-tion” and “Exceptional Principal” concepts have also been taken into account.
