【摘要】：為了降低譜失真,提出了一種基于隱馬爾科夫模型的窄帶語音帶寬擴展算法。首先,算法選取與寬帶譜包絡互信息大的參數(shù)構成特征矢量,并利用隱馬爾可夫狀態(tài)和過去觀察特征矢量的聯(lián)合先驗概率估計條件后驗概率。其次,以條件后驗概率為基礎,算法結合貝葉斯條件參數(shù)估計法和最小均方差準則估計寬帶譜包絡。針對寬帶激勵信號估計,基于信號高頻和低頻的諧波相關性,提出了一種中頻激勵擴展算法。實驗結果表明,與傳統(tǒng)的基于隱馬爾可夫模型的帶寬擴展算法相比,本文算法可降低0.187 dB的平均譜失真,將譜失真大于10 dB的語音幀減少了34.3%。
[Abstract]:In order to reduce spectral distortion, a narrow band speech bandwidth expansion algorithm based on hidden Markov model is proposed. Firstly, the parameters with large mutual information of wideband spectrum envelope are selected to form the feature vector, and the conditional posteriori probability of joint prior probability estimation of hidden Markov state and past observation feature vector is used. Secondly, based on conditional posteriori probability, the algorithm combines Bayesian conditional parameter estimation and minimum mean square error criterion to estimate wideband spectral envelope. For wideband excitation signal estimation, an intermediate frequency excitation expansion algorithm is proposed based on the correlation between high frequency and low frequency harmonics. The experimental results show that compared with the traditional bandwidth expansion algorithm based on hidden Markov model, the proposed algorithm can reduce the average spectral distortion of 0.187 dB and reduce the speech frames with spectral distortion greater than 10 dB by 34.3 points.
【作者單位】： 北京大學信息科學技術學院;深港產(chǎn)學研基地深圳市智能媒體和語音重點實驗室;
【分類號】：TN912.3

【參考文獻】

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本文編號：2339596