Enhanced voice activity detection in kernel subspace domain

Kim, Dong Kook; Shin, Jong Won; Chang, Joon-Hyuk

doi:10.1121/1.4809770

Cited by 2 publications

(1 citation statement)

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“…Recent work in automatic VOT detection has achieved accuracy levels that are similar to human precision levels by combining multidimensional feature extraction from speech signals and machine learning (Lin & Wang, 2011 ; Sonderegger & Keshet, 2012 ). Similarly, feature extraction and machine learning techniques have been applied to improve the performance of VAD in several fields (Kim, Chin, & Chang, 2013 ; Park et al 2014 ). These studies therefore support the present approach toward the automatic extraction of onset latencies from human speech in the context of behavioral experiments.…”

Section: Discussionmentioning

confidence: 99%

Chronset: An automated tool for detecting speech onset

2016

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The analysis of speech onset times has a longstanding tradition in experimental psychology as a measure of how a stimulus influences a spoken response. Yet the lack of accurate automatic methods to measure such effects forces researchers to rely on time-intensive manual or semiautomatic techniques. Here we present Chronset, a fully automated tool that estimates speech onset on the basis of multiple acoustic features extracted via multitaper spectral analysis. Using statistical optimization techniques, we show that the present approach generalizes across different languages and speaker populations, and that it extracts speech onset latencies that agree closely with those from human observations. Finally, we show how the present approach can be integrated with previous work (Jansen & Watter Behavior Research Methods, 40:744–751, 2008) to further improve the precision of onset detection. Chronset is publicly available online at www.bcbl.eu/databases/chronset.Electronic supplementary materialThe online version of this article (doi:10.3758/s13428-016-0830-1) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Chronset: An automated tool for detecting speech onset

2016

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An Adaptive Voice Activity Detection Algorithm

Zhang

Huang

2015

International Journal on Smart Sensing and Intelligent Systems

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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. Largescale 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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Enhanced voice activity detection in kernel subspace domain

Cited by 2 publications

References 10 publications

Chronset: An automated tool for detecting speech onset

Chronset: An automated tool for detecting speech onset

An Adaptive Voice Activity Detection Algorithm

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