Abstract: A pulse train of primary pulses is estimated from an inverse LPC analysis of a frame of voiced speech. From this estimated pulse train a pole-zero filter is estimated. The estimated pulse train is used to excite the estimated pole-zero filter to produce a synthesized speech signal. The synthesized speech signal is compared to the original frame of speech to determine the error in the original speech signal. Both the pulse amplitude and filter are adjusted to compensate for the error and another synthesized speech signal is produced. The process may be repeated until the synthesized speech signal and original speech signal converge.

Abstract: A two stage classification process is used in a speech recognition system. In the first stage, a slope vector template is generated from an extended LPC analysis using a universal bandwidth expansion technique. Using a dynamic programming technique, that first vector template identifies a subset of the overall vocabulary of the system. The speech signal is inverse filtered using the slope vector and a second LPC analysis is performed on the slope removed speech. The LPC vector is applied to an all-pass filter for initial nonlinear spectral shift of the speech. Final classification is then based on a normalizing spectral warp routine within a dynamic time warp program. The spectral warp is based on a closed form, near log transformation.