首页    期刊浏览 2024年10月06日 星期日
登录注册

文章基本信息

  • 标题:AN ADAPTIVE VOICE ACTIVITY DETECTION ALGORITHM
  • 本地全文:下载
  • 作者:Zhang Zhigang ; Huang Junqin
  • 期刊名称:International Journal on Smart Sensing and Intelligent Systems
  • 印刷版ISSN:1178-5608
  • 出版年度:2015
  • 卷号:8
  • 期号:4
  • 页码:2175-2194
  • 出版社:Massey University
  • 摘要:Voice Activity Detection (VAD) is a crucial step for speech processing, which detecting accuracy and speed directly affects the effect of subsequent processing. Some voice processing system based phone or in the indoor environment, which need simple and quick method of VAD, for these representative voice signal, this paper proposes a new algorithm which is adaptive and quick based on a major improvement to Dual-Threshold endpoint detection algorithm. First the amplitude normalization is processed to the original voice signal, the characteristic is extracted by means of short-time amplitude, which can simplify operation. Then, large-scale (long frame-length and frame-shift) short-time amplitude is used for rough detection, combining adaptive threshold judgement of consecutive frames, which can find voice areas of start-point and end-point quickly. To these areas, small-scale (short frame-length and frame-shift) short-time amplitude is used for accurate detection, forward scanning is put to start-point area, reverse scanning is put to end-point area, combining adaptive threshold judgement of consecutive frames, start-point and end-point of the effective speech can be accurately located. Experimental results show that the method of this paper can detect endpoints of voice signal more quickly and accurately, which can improve recognition performance dramatically. Large- scale can increase detection speed, small-scale can improve detection accuracy, both can be adjusted to satisfy the different requirements. The method of this paper ensures both detection speed and precision, which has more flexibility and applicability.
  • 关键词:Voice signal; Endpoint detection; Short-time amplitude; Multi-scale detection; Adaptive ; threshold
国家哲学社会科学文献中心版权所有