JPH0158519B2 - - Google Patents

Abstract

PURPOSE:To maintain a high recognition rate by correcting a value of tolerance weight in the direction for reducing a difference between the first rank of a weighted similarity degree and a weighted similarity degree in a dictionary item of an input word, whenever an erroneous recognition is generated, and using its value in the next time and thereafter. CONSTITUTION:When a word A is at the first rank of a similarity degree, for instance, if a value of tolerance weight applied to a similarity degree of a word B is an (n) point, and a result of work recognition is A against an input of B, the previous value of tolerance weight is corrected automatically to (n)+(m) points. Also, in case when a correct recognition result is obtained against an input of A and B, a value of tolerance weight remains as it is. On the other hand, in case when the result of word recognition becomes B against the input of A, on the contrary, the value of tolerance weight is corrected to (n)-(m). By continuing it, the value of tolerance weight is converged to a value by which a recognition rate as a whole becomes the highest.

Description

[Detailed description of the invention]

INDUSTRIAL APPLICATION FIELD The present invention relates to a word speech recognition method for recognizing words by comparing input speech with a word dictionary in which phonemes are expressed. Configuration of conventional example and its problems A conventional word speech recognition method will be explained with reference to figures. As shown in the figure, the input speech is first analyzed, the features of this input word speech are extracted, and
Recognize the phonemes that make up the input word sound. This recognized phoneme sequence is compared with the dictionary phoneme sequence of each dictionary item in the word dictionary, and the degree of similarity between the two phoneme sequences is calculated for each phoneme using a confusion matrix (CM) between phonemes. It is calculated by finding the recognition probability, and then a likelihood weight predetermined for each combination of each dictionary item and the dictionary item with the first similarity is applied to the similarity for each dictionary item. Addition or subtraction is performed, and the dictionary item for which the obtained weighted similarity is the maximum is determined as a recognized word. Table 1 shows an example of a word dictionary used in the word speech recognition method, and each word is written according to the phoneme notation shown in Table 2.

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Claims (1)

[Scope of Claims] 1. When recognizing a word by calculating the degree of similarity between the input voice and the dictionary phoneme series of each dictionary entry of a word dictionary in which phonemes are expressed, when the degree of similarity is calculated for the input voice, The similarity of each dictionary item is weighted by adding, subtracting, multiplying, or dividing a predetermined likelihood weight for each combination of each dictionary item and the dictionary item with the highest similarity. In a word speech recognition method in which similarity is calculated and the dictionary entry with the highest weighted similarity is used as a recognized word, the likelihood weight value is set to the weight at that time each time a word is misrecognized. 1. A word speech recognition method, characterized in that the difference between a weighted similarity of first place and a weighted similarity of an input word in a dictionary entry is automatically corrected in a direction that reduces the difference.