Speech analysis/synthesis/conversion by using sequential processing

This paper presents a method for speech analysis/synthesis/conversion by using sequential processing. The aims of this method are to improve the quality of synthesized speech and to convert the original speech into another speech of different characteristics. We apply the Kalman filter for estimating the auto-regressive coefficients of 'time varying AR model with unknown input (ARUI model)', which we have proposed to improve the conventional AR model, and we use a band-pass filter for making 'a guide signal' to extract the pitch period from the residual signal. These signals are utilized to make the driving source signal in speech synthesis. We also use the guide signal for speech conversion, such as in pitch and utterance length. Moreover, we show experimentally that this method can analyze/synthesize/convert speech without causing instability by using the smoothed auto-regressive coefficients.

[1]  Dimitrie C. Popescu,et al.  Kalman filtering of colored noise for speech enhancement , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[2]  G. Kitagawa,et al.  A smoothness priors time-varying AR coefficient modeling of nonstationary covariance time series , 1985, IEEE Transactions on Automatic Control.

[3]  Arijit K. Mahalanabis.  Introduction to random signal analysis and kalman filtering: Robert G. Brown , 1986, Autom..

[4]  C. Braun,et al.  Adaptive AR modeling of nonstationary time series by means of Kalman filtering , 1998, IEEE Transactions on Biomedical Engineering.

[5]  Samuel D. Stearns,et al.  Signal processing algorithms using Fortran and C , 1992 .

[6]  Robert Grover Brown,et al.  Introduction to random signal analysis and Kalman filtering , 1983 .