Discriminant local information distance preserving projection-based speaker confirmation method

Abstract

The present invention provides a discriminant local information distance preserving projection-based speaker confirmation method and belongs to the voiceprint recognition, pattern recognition and machine learning field. According to the method, speech data are obtained in a training phase; the i-vector of each training speech datum is extracted; the i-vector of each speaker is extracted accordingto training speech data corresponding to each speaker; training is performed, so that a discriminant local preserving projection matrix is obtained; during a speaker confirming phase, speech data to be tested are acquired, one speaker of the training speech data is selected, a distance between the speech data to be tested and the i-vector of the speaker is calculated; and if the distance is smaller than a preset distance threshold value, it is determined that the speech data to be tested belong to the speaker; and the confirmation of the speaker is completed. The method of the invention has high applicability, focuses on heterogeneous neighbor points, enhances the discrimination of the easily-confounded speech of speakers, has better distinguishing capability and improves the accuracy of speaker confirmation.

Description

technical field [0001] The invention belongs to the technical fields of voiceprint recognition, pattern recognition and machine learning, and in particular relates to a speaker confirmation method for discriminative local information distance-preserving mapping. Background technique [0002] Speaker confirmation refers to confirming the identity of the speaker based on the speaker-related information contained in the voice. With the rapid development of information technology and communication technology, speaker confirmation technology has received more and more attention and has been widely used in many fields. application. Such as identification, apprehension of criminals in the telephone channel, identity confirmation in court based on telephone recordings, telephone voice tracking, and anti-theft door opening functions. In the field of Internet applications and communications, speaker confirmation technology can be applied to voice dialing, telephone banking, telephone...

AI Technical Summary

Problems solved by technology

2) Low dimensionality

However, the i-vector / PLDA technology also has shortcomings: this method maximizes the distance between all projected heterogeneous points, without focusing on the confusing speech from different classes but very similar distances, and the distinction of confusing speech Need to be improved; global linearity is also a constraint for the continued development of this technology

Compared with the traditional principal component analysis (PCA), linear discriminant analysis (LDA), and probabilistic linear discriminant analysis (PLDA) dimensionality reduction methods (please give the corresponding Chinese name), manifold learning has achieved very good results in pattern recognition problems. Recognition effect, but immature in the field of speaker verification

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