Modeling method and modeling device for language identification

Abstract

The embodiment of the invention provides a modeling method for language identification, which comprises the following steps of: inputting voice data, preprocessing the voice data to obtain a characteristic sequence, mapping a characteristic vector to form a super vector, performing projection compensation on the super vector, and establishing a training language model through an algorithm of a support vector machine; and adopting the steps to obtain a super vector to be measured of the voice to be measured, performing the projection compensation on the super vector to be measured, grading the super vector to be measured by utilizing the language model, and identifying language types of the voice to be measured. The embodiment of the invention also provides a modeling device for the language identification, which comprises a voice preprocessing module, a characteristic extraction module, a multi-coordinate system origin selection module, a characteristic vector mapping module, a subspace extraction module, a subspace projection compensation module, a training module and an identification module. According to the method and the device which are provided by the embodiment of the invention, information which is invalid to the identification in high-dimension statistics is removed, the correction rate of the language identification is improved, and the computational complexity on an integrated circuit is reduced.

Description

technical field [0001] The present invention relates to speech recognition, pattern recognition and signal processing, in particular, the present invention relates to a modeling method and device for language recognition. Background technique [0002] Language recognition refers to the technology of using machines to distinguish the language type of a given speech. Language recognition technology is the front end of the multilingual processing system, which can be used in voice humanized services, voice security monitoring and other fields. [0003] At present, the most popular system modeling method in the field of language recognition is: extracting spectral layer features from the preprocessed speech, and then using GMM (Gaussian Mixture Models, Gaussian Mixture Model) or SVM (Support Vector Machine, Support Vector Machine) to carry out system modeling. [0004] Commonly used spectral layer features are Mel Frequency Cepstral Coefficients (MFCC), Linear Prediction Cepst...

AI Technical Summary

Problems solved by technology

[0010] 3) The subspace projection and subspace compensation techniques of GMM-SVM are both based on linear space, and the nonlinear information implied by the time series is not effectively utilized

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