Interpolation of linear prediction coefficients for speech coding
Tamanna Islam · Library and Archives Canada (Government of Canada) · 2000
Speech coding algorithms have different dimensions of performance. Among them, speech quality and average bit rate are the most important performance aspects. The purpose of the research is to improve the speech quality within the constraint of a low bit rate. Most of the low bit rate speech coders employ linear predictive coding (LPC) that models the short-term spectral information as an all-pole filter. The filter coefficients are called linear predictive (LP) coefficients. The LP coefficients are obtained from standard linear prediction analysis, based on blocks of input samples. In transition segments, a large variation in energy and spectral characteristics can occur in a short time interval. Therefore, there will be a large change in the LP coefficients in consecutive blocks. Abrupt changes in the LP parameters in adjacent blocks can introduce clicks in the reconstructed speech. Interpolation of the filter coefficients results in a smooth variation of the interpolated coefficients as a function of time. Thus, the interpolation of the LP coefficients in the adjacent blocks provides improved quality of the synthetic speech without using additional information for transmission. The research focuses on developing algorithms for interpolating the linear predictive coefficients with different representations (LSF, RC, LAR, AC). The LP analysis has been simulated; and its performance has been compared by changing the parameters (LP order, frame length, window offset, window length). Experiments have been performed on the subframe length and the choice of representation of LP coefficients for interpolation. Simulation results indicate that speech quality can be improved by energy weighted interpolation technique.