• Title/Summary/Keyword: trellis coded vector quantization

Search Result 3, Processing Time 0.018 seconds

Block Constrained Trellis Coded Vector Quantization of LSF Parameters for Wideband Speech Codecs

  • Park, Jung-Eun;Kang, Sang-Won
    • ETRI Journal
    • /
    • v.30 no.5
    • /
    • pp.738-740
    • /
    • 2008
  • In this paper, block constrained trellis coded vector quantization (BC-TCVQ) is presented for quantizing the line spectrum frequency parameters of the wideband speech codec. Both a predictive structure and a safety-net concept are combined into BC-TCVQ to develop the predictive BC-TCVQ. The performance of this quantization is compared with that of the linear predictive coding vector quantizer used in the AMRWB codec, demonstrating reductions in spectral distortion.

  • PDF

AN EFFICIENT TRELLIS EXCITATION SPEECH CODING AT 4.8 KBPS (효율적인 4.8 KBPS Trellis Exicitation 음성부호화방식)

  • 강상원
    • Proceedings of the Acoustical Society of Korea Conference
    • /
    • 1994.06c
    • /
    • pp.210-213
    • /
    • 1994
  • In this paper, we present a combination of trellis coded vector quantization and code-excited linear prediction coding, termed trellis excitation coding, for an efficient 4.8 kbps speech coding system. A training sequence-based algorithm is developed for designing an otimized codebook subject to the TEC structure. Also, we discuss the trellis symbol release rules that avoid excessive encoding delay. Finally, simulation results for the TEC coder are given at bit rate of 4.8 kbps.

  • PDF

Design of a Quantization Algorithm of the Speech Feature Parameters for the Distributed Speech Recognition (분산 음성 인식 시스템을 위한 특징 계수 양자화 방식 설계)

  • Lee Joonseok;Yoon Byungsik;Kang Sangwon
    • The Journal of the Acoustical Society of Korea
    • /
    • v.24 no.4
    • /
    • pp.217-223
    • /
    • 2005
  • In this paper, we propose a predictive block constrained trellis coded quantization (BC-TCQ) to quantize cepstral coefficients for the distributed speech recognition. For Prediction of the cepstral coefficients. the 1st order auto-regressive (AR) predictor is used. To quantize the prediction error signal effectively. we use a BC-TCQ. The performance is compared to the split vector quantizers used in the ETSI standard, demonstrating reduction in the cepstral distance and computational complexity.