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A fast speaker identification method and system based on model growth clustering

A recognition method and model recognition technology, applied in speech analysis, instruments, etc., can solve the problems of long matching time and poor real-time performance, and achieve the effect of short matching time and good real-time performance

Inactive Publication Date: 2019-02-01
GUANGDONG UNIVERSITY OF FOREIGN STUDIES
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[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-mentioned prior art, and provide a fast speaker recognition method based on model growth clustering with short matching time and good real-time performance

Method used

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  • A fast speaker identification method and system based on model growth clustering
  • A fast speaker identification method and system based on model growth clustering
  • A fast speaker identification method and system based on model growth clustering

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Experimental program
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Embodiment 1

[0070] like 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 preprocessing and noise reduction processing on each voiceprint signal, and the preprocessing process sequentially includes pre-emphasis, framing, windowing and endpoint detection;

[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 process of pre-emphasis, 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 resulting signal is:

[0078] S2.2: divided into frames, the voiceprint signal is divided into several frames at int...

Embodiment 2

[0114] like image 3 As shown, a rapid speaker recognition system based on model growth clustering includes: a client, a network connection module and a server, and the client and the server are connected through the network connection module;

[0115] Clients include:

[0116] Voiceprint acquisition module: used to collect the voiceprint signals of multiple people including speakers 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 invention relates to the field of voiceprint recognition, and more particularly, to a method and system for fast speaker recognition based on model growth clustering. Background technique [0002] In the embedded operating system, the identification of the speaker's identity is realized by voice, which usually needs 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), and the training of the model adopts the EM algorithm. Generally, a triple of λ=(ω, μ, Σ) can be used to succinctly represent a Gaussian mixture model. The Gaussian mixture model uses the weighted combination of multiple Gaussian models to describe a speaker's speech model, and uses the local expected maximum algorithm EM to continuously update the system parameters to obtain an approximate mat...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G10L17/02G10L17/06
Inventor 张晶陈晓梅郑党
Owner GUANGDONG UNIVERSITY OF FOREIGN STUDIES
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