本文選題：人體發(fā)聲過(guò)程 + 音段韻律��； 參考：《計(jì)算機(jī)仿真》2017年02期

【摘要】：對(duì)人體語(yǔ)音征提取身份優(yōu)化驗(yàn)證,可為說(shuō)話(huà)人識(shí)別奠定基礎(chǔ)。進(jìn)行人體語(yǔ)音征提取身份驗(yàn)證時(shí),應(yīng)分析人體語(yǔ)音音段韻律特征矢量序列,提取最優(yōu)音段韻律的高維特征值和特征向量,但是傳統(tǒng)方法通過(guò)對(duì)標(biāo)注音節(jié)的持續(xù)采樣點(diǎn)數(shù)進(jìn)行分析完成檢測(cè),但是不能精確分析人體語(yǔ)音音段韻律特征矢量序列,無(wú)法準(zhǔn)確提取最優(yōu)音段韻律的高維特征值和特征向量,存在人體語(yǔ)音征提取身份驗(yàn)證誤差大的問(wèn)題。提出一種改進(jìn)混沌的人體語(yǔ)音征提取身份優(yōu)化驗(yàn)證方法。上述方法先融合于混沌理論采集人體發(fā)聲過(guò)程中音段韻律原始信號(hào),將原始韻律信號(hào)映射到高維空間實(shí)現(xiàn)音段韻律相空間重構(gòu),映射相空間中音段韻律間相鄰軌道發(fā)散的平均變化率,然后利用K-均值聚類(lèi)的方法對(duì)音段韻律的語(yǔ)音幀進(jìn)行聚類(lèi),獲取規(guī)范化的音段韻律特征矢量序列,將規(guī)范化的音段韻律特征矢量序列投影到音段韻律高維核空間中,提取最優(yōu)音段韻律的高維特征值和特征向量,依據(jù)人體語(yǔ)音征提取身份優(yōu)化驗(yàn)證,仿真結(jié)果證明,所提方法特征提取精確度高,能夠有效地提升人體語(yǔ)音征提取身份驗(yàn)證的辨識(shí)率。
[Abstract]:The identification optimization of human speech feature extraction can lay a foundation for speaker recognition. In the process of human speech feature extraction, we should analyze the sequence of prosodic vector of human speech segment, and extract the high dimensional characteristic value and feature vector of the optimal segment prosody. However, the traditional methods can not accurately analyze the sequence of prosodic feature vectors of human speech segments, and can not accurately extract the high dimensional characteristic values and feature vectors of the optimal segment prosody by analyzing the number of continuous sampling points of the tagged syllables, but the traditional methods can not accurately analyze the sequence of prosodic feature vectors of human speech segments. There is a problem of large error in the identification of human speech sign extraction. An improved chaotic identification method for human speech feature extraction is proposed. Firstly, the method is integrated into chaos theory to collect the prosody signal of segment in the process of human voice, and the original prosodic signal is mapped to the high-dimensional space to reconstruct the phase space of segment prosody. In the mapping phase space, the average variation rate of the divergence of adjacent tracks between segments prosody is obtained, and then the speech frames of segment prosody are clustered by K-means clustering method, and the normalized segment prosodic characteristic vector sequences are obtained. The normalized segment prosodic feature vector sequence is projected into the segment prosodic high-dimensional kernel space, the high-dimensional characteristic value and eigenvector of the optimal segment prosody are extracted, and the identity optimization verification is extracted according to the human speech sign. The simulation results prove that, The proposed method has high accuracy of feature extraction and can effectively improve the identification rate of human speech feature extraction.
【作者單位】： 商丘學(xué)院計(jì)算機(jī)工程學(xué)院;蘭州理工大學(xué)理學(xué)院;
【分類(lèi)號(hào)】：TN912.3

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