Fine granularity scalable speech coding using embedded tree-structured vector quantization

This paper proposes an efficient codebook design for tree-structured vector quantization (TSVQ) that is embedded in nature. We modify two speech coding standards by replacing their original quantizers for line spectral frequencies (LSF’s) and/or Fourier magnitudes quantization with TSVQ-based quantizers. The modified coders are fine-granular bit-rate scalable with gradual change in quality for the synthetic speech. A fast search encoding algorithm using multistage tree-structured vector quantization (MTVQ) is proposed for quantization of LSF’s. The proposed method is compared to the multipath sequential tree-assisted search (MSTS) and to the well known multipath sequential search (MSS) or M-L search algorithms.

► An efficient codebook design for TSVQ based on the cell merging technique is proposed.
► Fine-granular bit-rate scalability is introduced in two speech coding standards by using a TSVQ-based embedded quantization.
► A fast search encoding algorithm using a MTVQ is proposed for quantization of LSF’s.
► Compared to the MSTS technique, the proposed MJLS algorithm performs better with significant reduction in complexity.
► Compared to the M-L algorithm, the MJLS algorithm can achieve a large reduction in the complexity with small degradation.