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Speech Periodicity Enhancement Based on Transform-domain Signal Decomposition and Robust Pitch Estimation.

机译：基于变换域信号分解和鲁棒基音估计的语音周期性增强。

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摘要

Periodicity is an important attribute of speech signals. It is an essential element of tonal languages, where the meaning of a word is determined by the pitch contour. Speech periodicity enhancement is the process of restoring waveform periodicity of noise-corrupted speech, in order to improve human perception of pitch and tone in noisy environments.;This thesis presents a novel approach to speech periodicity enhancement. The enhancement is achieved through periodic-aperiodic decomposition of the linear prediction residual signal in a transform domain. Transform coefficients that represent the periodic component are amplified to enhance the periodicity, and those coefficients representing the aperiodic components are attenuated to suppress the noise. We propose and evaluate different methods of assigning coefficient weights for periodicity enhancement. These methods include simple fixed weights, adaptive weights, and transform-domain Wiener filtering.;As a key component for periodic-aperiodic decomposition, a novel method of robust pitch estimation is developed. The temporally accumulated peak spectrum is proposed as a robust representation of speech harmonics. Gaussian mixture model is employed to model the effect of noise on the peak spectrum. Pitch estimation is formulated as a problem of l1-regularized maximum likelihood estimation, in which prior information is exploited. Two convex optimization approaches are developed to solve the associated non-convex optimization problem. The proposed pitch estimation method significantly outperforms the conventional methods. It attains high estimation accuracy for various types of noise at very low signal-to-noise ratio (e.g., -5 dB).;Experimental results confirm that with the proposed approach of periodicity enhancement, speech harmonic structure and waveform periodicity can be effectively restored. Compared with other speech and periodicity enhancement methods evaluated in this study, the proposed method can produce speech outputs with noticeably higher quality in terms of different objective measurements, such as SNR and PESQ.
机译：周期性是语音信号的重要属性。它是音调语言的基本元素，其中单词的含义由音高轮廓确定。语音周期性增强是恢复受噪声破坏的语音的波形周期性的过程，以改善人们在嘈杂环境中对音调和音调的感知。通过在变换域中线性预测残差信号的周期性非周期性分解来实现增强。代表周期分量的变换系数被放大以增强周期性，并且代表非周期分量的那些系数被衰减以抑制噪声。我们提出并评估了分配系数权重以增强周期性的不同方法。这些方法包括简单的固定权重，自适应权重和变换域维纳滤波。作为周期非周期分解的关键组成部分，开发了一种新颖的鲁棒基音估计方法。建议将时间累积的峰值频谱作为语音谐波的可靠表示。采用高斯混合模型来模拟噪声对峰频谱的影响。基音估计被公式化为l1正则化的最大似然估计的问题，其中利用了先验信息。开发了两种凸优化方法来解决相关的非凸优化问题。提出的音高估计方法明显优于传统方法。在极低的信噪比（例如-5 dB）下，它对各种类型的噪声都具有很高的估计精度。实验结果证实，通过所提出的周期性增强方法，可以有效地恢复语音谐波结构和波形周期性。与本研究中评估的其他语音和周期性增强方法相比，该方法可以产生质量更高的语音输出，这取决于不同的客观测量，例如SNR和PESQ。

著录项

  • 作者

    Huang, Feng.;

  • 作者单位

    The Chinese University of Hong Kong (Hong Kong).;

  • 授予单位 The Chinese University of Hong Kong (Hong Kong).;
  • 学科 Engineering Electronics and Electrical.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 156 p.
  • 总页数 156
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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