语音中的时变自回归：检测理论与应用
Time-Varying Autoregressions in Speech: Detection Theory and Applications
摘要：
本文提出了一种基于时变自回归模型的通用语音分析检测理论，该理论本身推广了经典的线性预测语音分析框架。这个理论导致一个计算上有效的决策理论的程序，可应用于检测语音波形数据中的声道变化的存在。相应的广义似然比检验推导和研究经验短的数据记录，使用共振峰状合成的例子，渐近，导致恒定的虚警率假设检验声道配置的变化。两个深入的案例研究，然后用来说明这个程序在不同的时间尺度的语音动力学的实际效果：第一，共振峰的变化检测的规模上的几十毫秒的数据，第二，识别声门打开和关闭的时刻的时间尺度低于10毫秒。
英文摘要：
This article develops a general detection theory for speech analysis based on time-varying autoregressive models, which themselves generalize the classical linear predictive speech analysis framework. This theory leads to a computationally efficient decision-theoretic procedure that may be applied to detect the presence of vocal tract variation in speech waveform data. A corresponding generalized likelihood ratio test is derived and studied both empirically for short data records, using formant-like synthetic examples, and asymptotically, leading to constant false alarm rate hypothesis tests for changes in vocal tract configuration. Two in-depth case studies then serve to illustrate the practical efficacy of this procedure across different time scales of speech dynamics: first, the detection of formant changes on the scale of tens of milliseconds of data, and second, the identification of glottal opening and closing instants on time scales below ten milliseconds.
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