In this paper, the predictive block-constrained trellis coded vector quantization (BC-TCVQ) schemes are developed for quantization of multiple frames line spectral frequency (LSF) parameters. The consecutive LSF frames are interleaved to subvectors for trellis modeling. The predictive BC-TCVQ systems are then designed to encode multi-frame LSF parameters. The performance evaluation of proposed schemes is compared with the single-frame LSF encoding methods using multi-stage vector quantization (MSVQ) and predictive block-constrained trellis coded quantization (BC-TCQ), demonstrating significant reduction of bit rate for transparent coding. The developed multi-frame LSF quantization schemes show satisfactory performance even at very low encoding rate, and thus can be efficiently applied to the speech coders with moderate delay.

Multi-frame Coding of LSF Parameters Using Block-Constrained Trellis Coded Vector Quantization.

This paper presents a multi-frame quantization of line spectral frequency (LSF) parameters using a deep autoencoder (DAE) and pyramid vector quantizer (PVQ). The object is to provide sophisticated LSF quantization for the ultra-low bit rate speech coders with moderate delay. For the compression and de-correlation of multiple LSF frames, a DAE possessing linear coder-layer units with Gaussian noise is used. The DAE demonstrates a high degree of modelling flexibility for multiple LSF frames. To quantize the coder-layer vector effectively, a PVQ is considered. Comparing the discrete cosine model (DCM), the DAE-based compression shows better modelling accuracy of multi-frame LSF parameters and possesses an advantage in that the coder-layer dimensions could be any value. The compressed coder-layer dimensions of the DAE govern the trade-off between the modelling distortion and the coder-layer quantization distortion. The experimental results show that the proposed algorithm with determined optimal coder-layer dimension outperforms the DCM-based multi-frame LSF quantization approach in terms of spectral distortion (SD) performance and robustness across different speech segments.

Multi-frame Quantization of LSF Parameters Using a Deep Autoencoder and Pyramid Vector Quantizer.

In order to meet the bandwidth requirements of low-bit-rate speech communication, there is an improvement of standard Mixed Excitation Linear Prediction (MELP) encoder and a 600bps MELP vocoder algorithm with higher quality is proposed. The algorithm forms a super frame by using 5 sub-frames. Joint quantization was carried out to remove redundancy information between intra-frames and inter-frames. We use the method of predictive split vector quantization to quantize the Line Spectral Frequency (LSF) parameters and the other parameters are jointly quantized. Compared to standard 2.4kbps MELP vocoder, the Mean Opinion Score(MOS) of the synthetic speech which is synthesized by the vocoder using the proposed algorithm is 2.54, and the synthetic speech quality is good.

http://dpi-proceedings.com/index.php/dtetr/article/download/7747/7324

A 600bps Vocoder Algorithm Based on MELP

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A 600bps Vocoder Algorithm Based on MELP