Tracking Formant Trajectory of Continuous Chinese Whispered Speech with Hidden Dynamic Model Based on Dynamic Target Orientation

Gang Lv, Heming Zhao
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引用次数: 3

Abstract

Aimed at the characteristics of Chinese whispered speech formants, i.e., migrating to highfrequency, increased bandwidth, and increased spurious peaks and merged peaks, a method of tracking the formant trajectory of continuous Chinese whispered speech using the Hidden Dynamic Model (HDM) with dynamic target orientation was put forward in this study. The calculation proceeded as follows: firstly, the PIF-LPC algorithm was used to evaluate the formant parameters of whispered speech (PIF-LPC is an improved LPC algorithm. In PIF-LPC, pole interaction factors are used to correct the formant bandwidth of residual poles, to reduce the effect of pole intersection and to improve the accuracy of formant parameters); then, the extracted formant parameters as dynamic target orientation were introduced in HDM and compared with the actual observation results for realtime adjustment of the weight of dynamic target orientation; finally, HDM was solved through auxiliary particle filtering (APF), so as to realize the tracking of the formant trajectory of whispered speech. It was shown in the experimental results that the interferences of spurious peaks and merged peaks were avoided when the formant trajectory of continuous whispered speech was tracked by this method.
基于动态目标方向的汉语连续耳语语音隐藏动态模型的形成峰轨迹跟踪
针对汉语耳语语音共振峰向高频偏移、带宽增大、杂峰和合并峰增多等特点,提出了一种基于动态目标定向的汉语耳语语音隐藏动态模型(Hidden Dynamic Model, HDM)跟踪连续语音共振峰轨迹的方法。计算过程如下:首先,采用PIF-LPC算法对低声语音的形成峰参数进行评估(PIF-LPC是一种改进的LPC算法)。在PIF-LPC中,利用极点相互作用因子对剩余极点的形成峰带宽进行校正,以减小极点相交的影响,提高形成峰参数的精度;然后,将提取的编队参数作为动态目标定向引入到HDM中,并与实际观测结果进行对比,实时调整动态目标定向权值;最后,通过辅助粒子滤波(APF)对HDM进行求解,实现耳语语音形成峰轨迹的跟踪。实验结果表明,该方法在跟踪连续耳语语音的形成峰轨迹时,避免了杂峰和合并峰的干扰。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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