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In this work we introduce an efficient method to reduce the coding rate of the spectral information in an algebraic code-excited linear prediction (ACELP) wideband codec. The Bimodal Vector Quantization (BMVQ) exploits the interframe correlation in spectral information to reduce the coding rate while maintaining high coded speech quality. In the BMVQ training phase, two codebooks are separately designed for voiced and unvoiced speech. For each speech frame, the optimal codebook for the search procedure is selected according to the interframe correlation of the spectral information. The BMVQ was successfully implemented in an ACELP wideband coder. The objective and subjective performance were found to be comparable to that of the combination of the split vector quantization and multistage vector quantization at 2.3 kbit/s.

BIMODAL QUANTIZATION OF WIDEBAND SPEECH SPECTRAL INFORMATION