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Abstract

For speech recognition, notably the recognition of curtly spoken speech with a large vocabulary, language models which take into account the probabilities of occurrence of word sequences are used so as to enhance the recognition reliability. These language models are determined from rather large quantities of text and hence represent an average value formed over several texts. However, the language model hence is not adapted very well to particularities of a special text. In order to achieve such adaptation of an existing language model to a special text while using only a small quantity of text, the invention proposes to determine confidence intervals from the counts of the word sequences occurring in the short text; this determination is possible by using calculation methods which are known from statistics. Subsequently, for each predecessor word sequence there is determined a scaling factor which adapts the language model values for all words in such a manner that as many adapted language model values as possible are situated within the confidence intervals. If scaled language model values are situated outside associated confidence intervals after the adaptation, the nearest boundaries of the confidence intervals are used as adapted language model values.

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First Claim