Text-related speaker recognition method based on infinite-state hidden Markov model

Abstract

The invention discloses a text-related speaker recognition method based on an infinite-state hidden Markov model, which can be used for solving the problem that overfitting or underfitting data is easily generated in the traditional hidden Markov model. The text-related speaker recognition method disclosed by the invention comprises the following steps of: firstly, carrying out preprocessing and feature extraction on a voice signal set for training; then, describing the set for training in a training process by adopting the infinite-state hidden Markov model, wherein the model has an infinite state number before training data arrives and an output probability distribution function corresponding to each state is expressed by using a student's t mixed model; after the training data arrives, calculating to obtain a parameter value in the model and the distribution condition of random variables; and during recognition, calculating a likelihood value related to each trained speaker model on the basis of recognizable voices subjected to the processing and feature extraction, wherein a speaker corresponding to the maximal likelihood value is used as a recognition result. The method disclosed by the invention can be used for effectively improving the recognition accuracy rate of a text-related speaker recognition system, and in addition, the text-related speaker recognition system has better robustness for noises.

Description

technical field [0001] The invention relates to the fields of signal processing and pattern recognition, and mainly relates to a text-related speaker recognition method based on an infinite state hidden Markov model. Background technique [0002] In access control, credit card transactions and court evidence, automatic speaker recognition, especially text-related speaker recognition, plays an increasingly important role. Its goal is to correctly determine the speech to be recognized as belonging to the speech library One of many references. [0003] In terms of text-related speaker recognition methods, the traditional Hidden Markov Model (GHMM) method has attracted more and more attention. Because of its high recognition rate, simple training, and small training data requirements, it has become a The current mainstream recognition method for text-related speaker recognition. Since GHMM has a good ability to represent the distribution of data, as long as there are enough st...

AI Technical Summary

Problems solved by technology

First of all, the number of states of the traditional GHMM is limited, which is preset before training, and is fixed during the training process, which easily causes the model to overfit or underfit the training data.

Secondly, the output probability distribution function corresponding to each state in the traditional GHMM is represented by a Gaussian mixture model. In practical applications, a disadvantage of the Gaussian mixture model is that it is prone to noise and outliers in the data acquisition process. Points are less robust

The above problems often make the recognition accuracy rate of the traditional Hidden Markov Model-based text-related speaker recognition system poor

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