窄带语音带宽扩展算法研究

Narrowband speech wideband extension algorithm research

为了降低谱失真,提出了一种基于隐马尔科夫模型的窄带语音带宽扩展算法。首先,算法选取与宽带谱包络互信息大的参数构成特征矢量,并利用隐马尔可夫状态和过去观察特征矢量的联合先验概率估计条件后验概率。其次,以条件后验概率为基础,算法结合贝叶斯条件参数估计法和最小均方差准则估计宽带谱包络。针对宽带激励信号估计,基于信号高频和低频的谐波相关性,提出了一种中频激励扩展算法。实验结果表明,与传统的基于隐马尔可夫模型的带宽扩展算法相比,本文算法可降低0.187 dB的平均谱失真,将谱失真大于10 dB的语音帧减少了34.3%。

To reduce the spectral distortion,a Hidden Markov Model-based narrowband speech bandwidth extension algorithm is presented.Firstly,the parameters which have higher mutual information with wideband envelope are extracted to constitute the feature vector,and then a posterior probability is calculated via the joint probability of the past observation feature vector sequence and the Markov states.Secondly,based on the posterior probability,the wideband envelope is estimated using Bayesian parameter estimation method and minimum mean square error criteria.For estimation of wideband excitation signal,intermediate frequency extension algorithm is presented based on the harmonic correlation between the low frequency and high frequency.The experimental results show that,compared with the traditional bandwidth extension algorithm based on Hidden Markov Model,the average spectral distortion is reduced by 0.187 dB and the number of speech frame with spectral distortion over 10 dB is decreased by 34.3%.