Speaker verification system creation method based on model sequence adaptive technique

Abstract

The invention discloses a speaker verification system creation method based on a model sequence adaptive technique. The method comprises the steps that voices of a first speaker to an nth speaker are entered sequentially, wherein j takes from 1 to n, and the following steps are executed: when the voice of the jth speaker is entered, a universal background model and an attenuation speaker model of the jth speaker are created, and the attenuation speaker models of all the created speakers are updated; after the voice of the nth speaker is entered, the universal background model of the nth speaker and the attenuation speaker models of the first speaker to the nth speaker are finally updated; and a speaker model is obtained to form a speaker verification system. The method is based on the initial universal background model, utilizes channel information and speaker sufficiency statistical information which are included in the sequentially entered voices of the n speakers, and sequentially adapts to the universal background model and the speaker model of the corresponding speaker, so that the speaker verification system used for speaker identity verification is created.

Description

Technical field [0001] The invention relates to a method for creating a speaker confirmation system for speaker identity confirmation based on a model sequence adaptive technology, and belongs to the technical field of computers and information services. Background technique [0002] Speaker confirmation technology is a type of biometric technology. It uses the information that characterizes the personality of the speaker contained in the voice waveform to confirm the identity of the speaker. Up to now, the speaker confirmation technology has been widely used in financial applications such as finance. Certification, public security criminal investigation and solving, national defense monitoring, personalized services and other fields. [0003] The speaker verification process mainly includes voiceprint modeling and recognition. Currently, voiceprint modeling usually adopts the Gaussian Mixture Model and the Universal Background Model (GMM-UBM) framework, that is, the universal bac...

AI Technical Summary

Problems solved by technology

Once the channel changes, the original general background model will inevitably have a channel mismatch problem, resulting in a decline in speaker recognition performance

However, retraining the general background model, on the one hand, obtaining a large number of speaker voices consistent with the new channel will consume a lot of real-time performance of the speaker confirmation system, and will waste a lot of manpower and material resources; Consistent speaker voice is difficult, sometimes impossible

At present, the speaker confirmation techniques for channel compensation mainly include eigenchannel, factor analysis, etc., but these techniques require a large amount of predicted channel data for calculation, so it is very difficult to implement

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