Narrowband speech wideband extension algorithm research

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%.