AN ADAPTIVE VOICE ACTIVITY DETECTION ALGORITHM

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International Journal on Smart Sensing and Intelligent Systems

Professor Subhas Chandra Mukhopadhyay

Exeley Inc. (New York)

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic

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VOLUME 8 , ISSUE 4 (December 2015) > List of articles

AN ADAPTIVE VOICE ACTIVITY DETECTION ALGORITHM

Correspondence
zzg@xaut.edu.cn '> Zhang Zhigang * / Correspondence
HUANGJQ@xaut.edu.cn '> Huang Junqin *

Keywords : Voice signal, Endpoint detection, Short-time amplitude, Multi-scale detection, Adaptive threshold.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 8, Issue 4, Pages 2,175-2,194, DOI: https://doi.org/10.21307/ijssis-2017-848

License : (CC BY-NC-ND 4.0)

Received Date : 10-May-2015 / Accepted: 10-November-2015 / Published Online: 01-December-2015

ARTICLE

ABSTRACT

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.

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REFERENCES

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