Abstract

A language model is a set of restrictions on the sequence of words allowed in a given language, and these restrictions can be expressed, for example, by the rules of a generative grammar or by a statistic of each pair of words evaluated in a given language. simple educational building. Although there are words with similar-sounding phonemes, it is usually not difficult for people to recognize the word. It mostly has to do with knowing the context and being very good at what words or phrases might be in it. The purpose of the language model is to provide context to the speech recognition system. The language model determines what words are allowed in the system language and in what order they can occur.Language models are trained, i.e., n-gram probabilities are estimated by observing sequences of words in a text corpus. Confusion reduction is typically performed on training data containing millions of word tokens. But, as has been observed, reducing confusion does not improve speech recognition results. Therefore, algorithms should be used that improve language models in terms of their impact on speech recognition, especially language models that determine the probability distribution of the speaker’s next spoken words given the speech history.In recent years, many speech recognition systems have been developed that use language models created for specific languages. And the use of language models in speech recognition serves to increase the efficiency of speech recognition. Many researchers have developed a traditional language model for the Uzbek language [8] –[12], but this model does not give the expected results. This requires the construction of other models for the Uzbek language. This article provides information about natural language, building natural language models, and applying them to speech recognition. Discusses research related to the construction of natural language models, problems that arise in the construction of statistical models, and approaches that can be used to solve them.

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