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Smoothing the Evolution of the Spectral Parameters in Speech Coders

New generation of speech coders have to achieve two goals: efficient use of bandwidth and high speech quality. The objective of this thesis is to improve the modelling of speech signal within the constraints of a low bit rate coder. Many speech coding algorithms use Linear Prediction (LP) coefficients to describe the power spectrum of the speech. These parameters are obtained for blocks of input samples using standard linear prediction analysis technique. Changes in the speech power spectrum results in the evolution of the LP parameters. However, conventional linear prediction analysis has shortcomings that contribute to the frame-to-frame variation of the LP parameters. These undesired variations affect the performance of the parameters coding and the perceptual quality of the synthesized signal. For voiced speech, efficient coding of the excitation pitch pulses relies on the similarity of successive pitch waveforms. The performance of this coding stage is also jeopardized by LP parameters variations. The goal of this thesis is to modify the traditional linear prediction analysis in such way that the fluctuations of the LP coefficients are reduced, while the pitch pulse shape evolves slowly. These modifications can lead to an increase in the coding efficiency and/or an improvement in the speech quality. Two different methods have been developed for this purpose. In the first approach we derive the LP parameters such that the glottal excitation model matches as closely as possible a target waveform. The latter contains slowly evolving pulses representing voiced speech excitation. The simulation results indicate that the target matching method results in an increase in the pitch prediction gain which is a measure of similarity of successive pitch pulses. The frame-to-frame variation of the LP coefficients is also lowered with respect to the conventional linear prediction analysis. In the second method, we enforce the smoothness on the evolution of LP parameters by directly including their variation in the LP error function. A novel scheme to dynamically control the contribution of this additional term is also proposed. Experiments indicate that this method can considerably reduce the fluctuation of LP parameters while the overall prediction gain of the LP filter is maintained.