Intraframe and Interframe Coding of Speech Spectral Parameters

Most low bit rate speech coders employ linear predictive coding (LPC) which models the short-term spectral information within each speech frame as an all-pole lter. In this thesis, we examine various methods that can e ciently encode spectral parameters for every 20 ms frame interval. Line spectral frequencies (LSF) are found to be the most e ective parametric representation for spectral coding. Product code vector quantization (VQ) techniques such as split VQ (SVQ) and multi-stage VQ (MSVQ) are employed in intraframe spectral coding, where each frame vector is encoded independently from other frames. Depending on the product code structure, \transparent coding" quality is achieved for SVQ at 26{28 bits/frame and for MSVQ at 25{27 bits/frame. Because speech is quasi-stationary, interframe coding methods such as predictive SVQ (PSVQ) can exploit the correlation between adjacent LSF vectors. Nonlinear PSVQ (NPSVQ) is introduced in which a nonparametric and nonlinear predictor replaces the linear predictor used in PSVQ. Regardless of predictor type, PSVQ garners a performance gain of 5{7 bits/frame over SVQ. By interleaving intraframe SVQ with PSVQ, error propagation is limited to at most one adjacent frame. At an overall bit rate of about 21 bits/frame, NPSVQ can provide similar coding quality as intraframe SVQ at 24 bits/frame (an average gain of 3 bits/frame). The particular form of nonlinear prediction we use incurs virtually no additional encoding computational complexity. Voicing classi cation is used in classi ed NPSVQ (CNPSVQ) to obtain an additional average gain of 1 bit/frame for unvoiced frames. Furthermore, switchedadaptive predictive SVQ (SA-PSVQ) provides an improvement of 1 bit/frame over PSVQ, or 6{8 bits/frame over SVQ, but error propagation increases to 3{7 frames. We have veri ed our comparative performance results using subjective listening tests.