Canon Kabushiki Kaisha

Asako Higuchi

Jason Peter Andrew Charlesworth

Philip Neil Garner

A dynamic programming technique is provided for matching two sequences of phonemes both of which may be generated from text or speech. The scoring of the dynamic programming matching technique uses phoneme confusion scores, phoneme insertion scores and phoneme deletion scores which are obtained in advance in a training session and, if appropriate, confidence data generated by a recognition system if the sequences are generated from speech.

Language recognition using a similarity measure

A system is provided for matching two or more sequences of phonemes both or all of which may be generated from text or speech. A dynamic programming matching technique is preferably used having constraints which depend upon whether or not the two sequences are generated from text or speech and in which the scoring of the dynamic programming paths is weighted by phoneme confusion scores, phoneme insertion scores and phoneme deletion scores where appropriate.

Pattern matching method and apparatus

A system is provided for comparing an input query with a number of stored annotations to identify information to be retrieved from a database. The comparison technique divides the input query into a number of fixed-size fragments and identifies how many times each of the fragments occurs within each annotation using a dynamic programming matching technique. The frequencies of occurrence of the fragments in both the query and the annotation are then compared to provide a measure of the similarity between the query and the annotation. The information to be retrieved is then determined from the similarity measures obtained for all the annotations.

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Tokyo, Japan

Asako Higuchi