Accurate vocal event detection method based on a fixed-point analysis of mapping from time to weighted average group delay
Accurate vocal event detection method based on a fixed-point analysis of mapping from time to weighted average group delay
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基于时间到加权平均群时延映射定点分析的准确声音事件检测方法
DOI：
10.21437/icslp.2000-899
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发表时间：
2000
期刊：
影响因子：
--
通讯作者：
Parham Zolfaghari
中科院分区：
文献类型：
--
作者：
Hideki Kawahara;Yoshinori Atake;Parham Zolfaghari
提出了一种基于群时延和不动点分析的事件检测和表征方法。该方法能够检测诸如声带闭合的语音事件的精确定时和传播。从高斯时间窗的中心到平均时间的映射提供事件位置作为其固定点。使用从幅度谱导出的最小相位群延迟函数来细化这些初始估计提供了事件位置和每个事件的激励持续时间的精确估计。所提出的算法进行了测试，同时记录的声音波形和EGG信号的合成语音样本和自然语音数据库。这些测试表明，所提出的方法提供了声带闭合瞬间的估计，其定时精度在60 µ st至210µs标准差内。该算法通过大量使用FFT而不引入任何迭代过程来实现，适合于实时操作。它是一个潜在的非常强大的工具，语音诊断和建设非常高质量的语音处理系统。
A new procedure for event detection and characterization is proposed based on group delay and fixed point analysis. This method enables the detection of precise timing and spread of speech events such as a vocal fold closure. A mapping from the center of a Gaussian time window to the mean time provides event locations as its fixed points. Refining these initial estimates using minimum phase group delay functions derived from the amplitude spectra provides accurate estimates of event locations and durations of excitations of each event. The proposed algorithm was tested using synthetic speech samples and natural speech database of simultaneously recorded sound waveforms and EGG signals. These tests revealed that the proposed method provides estimates of vocal fold closure instants with timing accuracy within 60 µ st o 210µs standard deviations. This algorithm is implemented to be suitable for real-time operation by making extensive use of FFTs without introducing any iterative procedures. It is potentially a very powerful tool for speech diagnosis and construction of very high quality speech manipulation systems.