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首页> 外文期刊>Computer speech and language >Automatic glottal inverse filtering with the Markov chain Monte Carlo method
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Automatic glottal inverse filtering with the Markov chain Monte Carlo method

机译：马尔可夫链蒙特卡罗方法自动进行声门逆滤波

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摘要

This paper presents a new glottal inverse filtering (GIF) method that utilizes a Markov chain Monte Carlo (MCMC) algorithm. First, initial estimates of the vocal tract and glottal flow are evaluated by an existing GIF method, iterative adaptive inverse filtering (IAIF). Simultaneously, the initially estimated glottal flow is synthesized using the Rosenberg-Klatt (RK) model and filtered with the estimated vocal tract filter to create a synthetic speech frame. In the MCMC estimation process, the first few poles of the initial vocal tract model and the RK excitation parameter are refined in order to minimize the error between the synthetic and original speech signals in the time and frequency domain. MCMC approximates the posterior distribution of the parameters, and the final estimate of the vocal tract is found by averaging the parameter values of the Markov chain. Experiments with synthetic vowels produced by a physical modeling approach show that the MCMC-based GIF method gives more accurate results compared to two known reference methods.
机译：本文提出了一种利用马尔可夫链蒙特卡罗（MCMC）算法的声门逆滤波（GIF）新方法。首先，通过现有的GIF方法（迭代自适应逆滤波（IAIF））评估声道和声门流量的初始估计。同时，使用Rosenberg-Klatt（RK）模型合成初始估计的声门流，并用估计的声道过滤器进行滤波以创建合成语音帧。在MCMC估计过程中，对初始声道模型的前几个极点和RK激励参数进行了优化，以使时域和频域中合成语音和原始语音信号之间的误差最小。 MCMC估计参数的后验分布，并通过平均马尔可夫链的参数值找到声道的最终估计值。通过物理建模方法生产的合成元音的实验表明，与两种已知的参考方法相比，基于MCMC的GIF方法可提供更准确的结果。

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  • 来源
    《Computer speech and language》 |2014年第5期|1139-1155|共17页
  • 作者

    Harri Auvinen; Tuomo Raitio; Manu Airaksinen; Samuli Siltanen; Brad H. Story; Paavo Alku;

  • 作者单位

    Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland;

    Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland;

    Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland;

    Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland;

    Department of Speech and Hearing Sciences, University of Arizona, AZ, USA;

    Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Glottal inverse filtering; Markov chain Monte Carlo;

    机译：声门逆滤波马尔可夫链蒙特卡洛;

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