Training method for language recognition model and language recognition method

Abstract

The invention relates to a training method for a language recognition model and a language recognition method. The language recognition method comprises the steps: extracting the phoneme posterior probability of speech data, converting the phoneme posterior probability into a log domain, conducting dimensionality reduction and mean and variance normalization, and then obtaining phoneme associated features; calculating Baum-Welch statistical magnitude by means of the phoneme associated features, and extracting a phoneme variance factor through the Baum-Welch statistical magnitude; modeling the phoneme variance factor, and establishing an SVM model (a language recognition model); and marking the SVM model by a phoneme variance factor of to-be-recognized speech data, conducting mean and variance normalization on the score, performing linear discriminant analysis and Gauss back end normalization on the normalized score to realize score correction, and finally obtaining a recognition result. Compared with a conventional language recognition method, the language recognition method of the invention has the advantages that the calculation complexity is reduced, the language recognition performance is obviously improved, and the method is highly practical.

Description

technical field [0001] The invention relates to a method for recognizing language information of voice data, and more specifically, the invention relates to a method for recognizing a language based on phoneme-related features. Background technique [0002] With the globalization of information in modern society, language recognition has become one of the research hotspots of speech recognition technology. The purpose of language recognition technology is to be able to manufacture a machine that imitates human thinking to a certain extent to identify the language of speech, that is, to extract the difference information of each language from the speech signal, and use this as a basis to judge the language it belongs to. The extracted speech signal features directly affect the result of language recognition. [0003] Mainstream language recognition technologies include two types: recognition based on acoustic spectrum features and recognition based on phoneme features. [0...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the defect that the traditional method based on acoustic spectrum features does not contain speech pronunciation information, overcome the defects of high computational complexity and long running time in decoding phoneme sequences based on the traditional method based on phoneme features, thereby providing a method that reduces computational complexity and improves Language Recognition Method for Recognition Performance

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