Title
An Excitation Model Based On Inverse Filtering For Speech Analysis And Synthesis
Abstract
Speech Synthesized in LPC-like vocoders suffered from a typical buzz problem. It is mostly due to the fact that the excitation is either a pulse train or a white Gaussian noise. In this paper, a new excitation model is proposed to reconstruct residual signal derived from inverse filtering. A residual frame of two-pitch periods length is intercepted to do spectrum analysis in every speech frame. Amplitude spectrum of only half of pitch period length is preserved in synthesis stage and zero-phase criterion is used to synthesize the excitation frame. Then the excitation signal is constructed by pitch-synchronous overlapping method (PSOLA). Speech synthesized by this excitation model can give a CMOS of 1.56 compared to the traditional excitation model. After that Mel Generalization Cepstrum (MGC) and LBG algorithm are adopted to manipulate the amplitude spectrum of proposed excitation model. MSE distortion and listening test showed that LBG algorithm is better than MGC to compress the amplitude spectrum.
Year
DOI
Venue
2011
10.1109/MLSP.2011.6064574
2011 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING (MLSP)
Keywords
Field
DocType
excitation model, inverse filtering, PSOLA, MGC, LBG
Cepstrum,Residual frame,Artificial intelligence,Distortion,PSOLA,Speech synthesis,Pattern recognition,Filter (signal processing),Algorithm,Speech recognition,Additive white Gaussian noise,Signal reconstruction,Mathematics
Conference
Volume
Issue
ISSN
null
null
2161-0363
Citations 
PageRank 
References 
4
0.43
8
Authors
2
Name
Order
Citations
PageRank
Zhengqi Wen141.44
Jianhua Tao2848138.00