Evaluation of Glottal Inverse Filtering Algorithms Using a Physiologically Based Articulatory Speech Synthesizer.
Evaluation of Glottal Inverse Filtering Algorithms Using a Physiologically Based Articulatory Speech Synthesizer.
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使用基于生理学的发音语音合成器评估声门逆滤波算法。
DOI：
10.1109/taslp.2017.2714839
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发表时间：
2017
期刊：
影响因子：
--
通讯作者：
Quatieri,ThomasF
中科院分区：
文献类型：
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作者：
Chien,Yu-Ren;Mehta,DaryushD;Guðnason,Jón;Zañartu,Matías;Quatieri,ThomasF
声门反滤波旨在从语音信号中估计出声门气流信号，用于说话人识别和临床语音评估等应用。尽管如此，由于直接测量声门气流的实际困难，反滤波算法的评估一直具有挑战性。除此之外，人们还认识到许多方法在语音条件下的性能会下降，例如呼吸，高音调，轻声和奔跑语音。本文提出了一个全面的，客观的，比较评价的最先进的反滤波算法，利用语音和声门气流信号产生的生理语音合成器。该合成器提供了基于物理的语音生成过程模拟，从而为揭示每种算法的时间和频谱性能特征提供了充分的测试平台。合成数据中包含连续的语音和持续的元音，这些语音具有多种语音质量（按压、轻微按压、模态、轻微呼吸和呼吸）、基本频率和声门下压力，以模拟真实语音中的自然变化。在评估声门流量估计的准确性时，使用了多种误差测量，包括测量总体波形偏差的估计信号中的误差，以及从声门流量估计中提取的几个临床相关特征中的每一个的误差。从声门流量估计实验中计算的波形误差显示，持续元音的平均值约为真实声门流量导数振幅的30%，连续语音的平均值约为40%。闭相方法在不同的声音质量和声门下压力下表现出显著的稳定性。根据显著性检验的建议，选择的算法是闭合相位协方差分析（用于分析持续元音）和稀疏线性预测（用于分析连续语音）。数据子集分析的结果表明，在声门流量估计中，分析紧密的圆形元音是一个额外的挑战。
Glottal inverse filtering aims to estimate the glottal airflow signal from a speech signal for applications such as speaker recognition and clinical voice assessment. Nonetheless, evaluation of inverse filtering algorithms has been challenging due to the practical difficulties of directly measuring glottal airflow. Apart from this, it is acknowledged that the performance of many methods degrade in voice conditions that are of great interest, such as breathiness, high pitch, soft voice, and running speech. This paper presents a comprehensive, objective, and comparative evaluation of state-of-the-art inverse filtering algorithms that takes advantage of speech and glottal airflow signals generated by a physiological speech synthesizer. The synthesizer provides a physics-based simulation of the voice production process and thus an adequate test bed for revealing the temporal and spectral performance characteristics of each algorithm. Included in the synthetic data are continuous speech utterances and sustained vowels, which are produced with multiple voice qualities (pressed, slightly pressed, modal, slightly breathy, and breathy), fundamental frequencies, and subglottal pressures to simulate the natural variations in real speech. In evaluating the accuracy of a glottal flow estimate, multiple error measures are used, including an error in the estimated signal that measures overall waveform deviation, as well as an error in each of several clinically relevant features extracted from the glottal flow estimate. Waveform errors calculated from glottal flow estimation experiments exhibited mean values around 30% for sustained vowels, and around 40% for continuous speech, of the amplitude of true glottal flow derivative. Closed-phase approaches showed remarkable stability across different voice qualities and subglottal pressures. The algorithms of choice, as suggested by significance tests, are closed-phase covariance analysis for the analysis of sustained vowels, and sparse linear prediction for the analysis of continuous speech. Results of data subset analysis suggest that analysis of close rounded vowels is an additional challenge in glottal flow estimation.