Speaker rapid identification method and system based on growing and clustering algorithm of models
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A recognition method and model recognition technology, applied in speech analysis, instruments, etc., can solve problems such as long matching time and poor real-time performance
Inactive Publication Date: 2015-11-25
GUANGDONG UNIVERSITY OF FOREIGN STUDIES
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[0004] The primary purpose of the present invention is to overcome the defects of long matching time and poor real-time performance described in the above-
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[0069] Example 1
[0070] Such as figure 1 As shown, a rapid speaker recognition method based on model growth clustering includes model training and model recognition;
[0071] Model training includes the following steps:
[0072] S1: Collect the voiceprint signals of multiple people including the speaker, that is, voice signals;
[0073] S2: Perform pre-processing and noise reduction processing on each voiceprint signal, and the pre-processing process includes pre-emphasis, framing, windowing and endpoint detection in turn;
[0074] In the specific implementation process, in step S2, preprocessing each voiceprint signal specifically includes the following steps:
[0075] S2.1: Pre-emphasis, in the pre-emphasis process, the voiceprint signal is moved to the appropriate frequency band through the filter,
[0076] The transfer function is: H(z) = 1-0.9375z -1 ,
[0077] The signal obtained is: S ~ ( n ) = S ( n ) - 0.9375 S ( n - 1 ) ;
[007...
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[0113] Example 2
[0114] Such as image 3 As shown, a rapid speaker recognition system based on model growth clustering includes: a client, a network connection module, and a server. The client and the server are connected through the network connection module;
[0115] The client includes:
[0116] Voiceprint acquisition module: used to collect voiceprint signals of multiple people including the speaker and output to the preprocessing module;
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Abstract
The present invention discloses a speaker rapid identification method and a system based on the growing and clustering algorithm of models. The method comprises the processes of model training and model identification. The model training process comprises the steps of acquiring voiceprint signals from multiple persons including a speaker, pre-treating all the voice-print signals and extracting voiceprint characteristic parameters to form a plurality of models, and conducting the adaptive classification for all models based on the growing and clustering algorithm of models. The model identification process comprises the steps of acquiring voice signals from a speaker, pre-treating the voice signals, extracting voiceprint characteristic parameters, calculating the characteristic parameters of to-be-identified voice signals and the likelihoods of all model types, selecting a model type for the to-be-identified voice signal based on the maximum likelihood principle, calculating the likelihood scores of all models in the above selected model type, and adopting a model of the highest likelihood score as an identification result. According to the technical scheme of the invention, the operation of matching the to-be-identified voice characteristics with all models is not required, so that the method is short in matching period and good in real-time performance. The method can be well adapted to large-scale model bases.
Description
Technical field [0001] The present invention relates to the field of voiceprint recognition, and more specifically, to a method and system for rapid speaker recognition based on model growth clustering. Background technique [0002] In the embedded operating system to realize the identification of the speaker's identity through voice, it is usually necessary to preprocess the input voiceprint, transmit the data to the server, and then generate the voiceprint model, match the model, and finally output and display the result. Among them, the voiceprint model refers to the Gaussian Mixture Model (GMM), which uses the EM algorithm for training. Generally, λ=(ω,μ,Σ) triples can be used to concisely express a Gaussian mixture model. The Gaussian mixture model uses a weighted combination of multiple Gaussian models to describe a speaker's speech model, and uses the local expected maximum algorithm EM to continuously update system parameters, thereby obtaining the approximate mathematic...
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