<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>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</p>
</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

An Adaptive Voice Activity Detection Algorithm

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"An Adaptive Voice Activity Detection Algorithm"}

Voice Activity Detection (VAD) is a crucial step for speech processing, which detecting accuracy and speed directly affects the effect of subsequent...

An Adaptive Voice Activity Detection Algorithm

Pubblicato online: 01 dic 2015

Pagine: 2175 - 2194

Ricevuto: 10 mag 2015

Accettato: 10 nov 2015

DOI: https://doi.org/10.21307/ijssis-2017-848

Parole chiave
Voice signal, Endpoint detection, Short-time amplitude, Multi-scale detection, Adaptive threshold

© 2015 Zhang Zhigang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

An Adaptive Voice Activity Detection Algorithm

Pubblicato online: 01 dic 2015

Pagine: 2175 - 2194

Ricevuto: 10 mag 2015

Accettato: 10 nov 2015

DOI: https://doi.org/10.21307/ijssis-2017-848

Parole chiaveVoice signal, Endpoint detection, Short-time amplitude, Multi-scale detection, Adaptive threshold

© 2015 Zhang Zhigang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
Voice signal, Endpoint detection, Short-time amplitude, Multi-scale detection, Adaptive threshold