Low complexity LSF quantization for wideband speech coding

State-of-the-art narrowband speech coders operating from 4 to 16 kbit/s are mostly based on the code-excited linear predictive (CELP) model. They achieve a good synthesis quality usually at the expense of a high coding complexity. For example, in the 8 kbit/s G.729 coder the innovation codebook search is responsible for approximately half the total coder complexity, the latter being close to 20 MIPS in fixed-point DSP implementation. Less known is the relative part of spectral quantization, which is around 8% of the total complexity. CELP coders are still relevant for wideband speech coding but their complexity is greater than in the narrowband case, which becomes critical for real-time implementations. We propose in this article a two-stage algebraic-stochastic line spectral frequency (LSF) quantization scheme. It combines the strengths of algebraic and stochastic techniques, namely low computation and storage cost and good performance. The generalized Lloyd-Max algorithm is adapted for optimizing lattice codebooks obtained by spherical truncation. Simulations with a Gaussian source show that the quantization method exhibits good quality/complexity tradeoffs. Several stochastic-algebraic LSF quantizers are derived and compared to a more conventional technique.