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Speaker Recognition on the Base of Time-Varying Characteristics of Speech Signal

Author: XuLiangJun
Tutor: FeiWanChun
School: Suzhou University
Course: Textile Engineering
Keywords: Time-varying characteristics Characteristic frequency Nonstationarity TVPAR model Speaker Recognition
CLC: TN912.34
Type: Master's thesis
Year: 2010
Downloads: 83
Quote: 0
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Abstract


Speaker recognition is a special kind of speech recognition. In recent years, the rapid development of this technology , and text dependent speaker recognition systems require identity verification in some places has been applied. But there are still some problems to be solved , where the key question is whether to use the speech signal which features describing speak talent is effective and reliable. Speaker recognition , including speaker verification and speaker identification , this paper is the study of the text relating to speaker identification problem . Based on time-varying characteristics of speech signal , the mean MEL cepstrum extracted based on the characteristic frequency variation with time (including the time-varying pitch frequency ), whereby the speech signal by the characteristic frequency of the respective inverted sequence of spectral values ??of the time series . Using time series preprocessing and mathematical statistics method , the separation time series trends and fluctuations in the amount of the amount of random fluctuations . The amount is zero-mean random fluctuations autocovariance nonstationary time series , the use of full- order time-varying parameter autoregressive (Time-Varying Parameter Autoregressive) model, the amount of sequence analysis of stochastic volatility , further extraction speaker speech feature parameters. Fluctuations in the amount of random sequence and with a full order TVPAR models were based on the analysis of speaker recognition . This selects the smallest BIC (Bayesian Information Criterion) law analysis to determine the order of regression model , the last speaker using Mahalanobis distance discriminant . Experimental results show that the model with a full order TVPAR recognition , the recognition rate than the amount of random fluctuations on the sequence recognition rate has improved greatly . In the full -order TVPAR based on the model , take a characteristic frequency of 97.3% recognition rate , two characteristic frequency identification rate of 98.6% .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Communicate > Electro-acoustic technology and speech signal processing > Speech Signal Processing > Speech Recognition and equipment
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