Self-Adaptive Vowel-Frame Detection Algorithm Based on Energy Distribution Analysis in Frequency Domain

QIAN Bo; LI Yan-ping; TANG Zhen-min; XU Li-min

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Self-Adaptive Vowel-Frame Detection Algorithm Based on Energy Distribution Analysis in Frequency Domain

更新时间：2025-07-16

- Self-Adaptive Vowel-Frame Detection Algorithm Based on Energy Distribution Analysis in Frequency Domain
- Acta Electronica Sinica Vol. 35, Issue 2, Pages: 279-282(2007)
- 作者机构：
  
  南京理工大学模式识别与智能系统实验室,江苏,南京,210094
- 作者简介：
- 基金信息：
- DOI：
  CLC： TN912.34
- Published：2007
- 稿件说明：
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QIAN Bo, LI Yan-ping, TANG Zhen-min, et al. Self-Adaptive Vowel-Frame Detection Algorithm Based on Energy Distribution Analysis in Frequency Domain[J]. Acta Electronica Sinica, 2007, 35(2): 279-282.
DOI：

QIAN Bo, LI Yan-ping, TANG Zhen-min, et al. Self-Adaptive Vowel-Frame Detection Algorithm Based on Energy Distribution Analysis in Frequency Domain[J]. Acta Electronica Sinica, 2007, 35(2): 279-282. DOI：

摘要

本文提出了一种基于频域能量分布分析的自适应元音帧提取算法.该方法采用MEL标度映射各频率分量

通过分析低频、高频能量的分布关系判定元音帧并计算出该帧的短时能量、短时平均过零率的值指导时域帧提取的方案.为了适应连续语音检测过程中响度大小的不断变化

文中选择了按概率抽样进行频域分析修正时域阈值的方式.这种方式利用较少抽样帧的变换分析指导时域检测

相对于全面变换分析降低了运算量

达到了实时检测的要求

同时提高了整体检测性能.实验表明

该方法总体运算量低

同时对发声过程中的音量变化有一定的自适应性.实验中

针对单字音元音帧的正确提取率达到了97%以上

平均丢帧率为3.95%;针对连续语音的正确提取率也达到了90%以上.

Abstract

This paper presents a self-adaptive vowel-frame detection algorithm based on energy distribution analysis in frequency domain.The method maps MEL-scale into frequency component and detect vowel-frame by compare the energy of LF and HF.Then conduct the time-domain-analysis with short-time energy and zero-crossing rate of vowel-frame

which have been verified.The method needs less computation cost and can adapt the transformation of volume.The experimental results on single word have demonstrated that the accurate rate is over 97% while the average frame-lost rate is 3.95%.The accurate rate of vowel-frame detection on continuous speech is exceeding 90%.

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