Analysis and improvement of a statistical model-based voice activity detector

Cho, Yong Duk; Kondoz, A.M.

doi:10.1109/97.957270

Cited by 84 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We consider two different pdfs as the candidate distributions for statistical modeling. The first one is the complex Gaussian pdf which is most widely applied to characterize the DFT coefficients' distribution in speech analysis [1,2,3,5,6,8,9,10,11,12] . With the Gaussian pdf assumption, the distributions of the noisy spectral components conditioned on both hypotheses are given by:…”

Section: Statistical Models For Noisy Speechmentioning

confidence: 99%

“…This algorithm has shown a high detection accuracy compared with the conventional algorithms. The statistical model-based method has been further improved with the incorporation of the soft decision scheme [2,3] . Most of the conventional VAD algorithms which mainly operate in the discrete Fourier transform (DFT) domain assume that the clean speech and noise spectra are characterized by Gaussian distributions.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Voice Activity Detection Based on Multiple Statistical Models

Yang

2011

AMR

View full text Add to dashboard Cite

One of the key issues in practical speech processing is to achieve robust voice activity detection (VAD) against the background noise. Most of the statistical model-based approaches have tried to employ the Gaussian assumption in the discrete Fourier transform (DFT) domain, which, however, deviates from the real observation. For a class of VAD algorithms based on Gaussian model and Laplacian model, we incorporate complex Laplacian probability density function to our analysis of statistical properties. Since the statistical characteristics of the speech signal are differently affected by the noise types and levels, to cope with the time-varying environments, our approach is aimed at finding adaptively an appropriate statistical model in an online fashion. The performance of the proposed VAD approaches in stationary noise environment is evaluated with the aid of an objective measure.

show abstract

Section: Statistical Models For Noisy Speechmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Voice Activity Detection Based on Multiple Statistical Models

Yang

2011

AMR

View full text Add to dashboard Cite

show abstract

“…In face‐to‐face collaboration, spoken interactions dominate the ways in which students demonstrate these collaborative activities. Capturing audio data from these natural group interactions provides a more in‐the‐moment description of student interactions compared to thoroughly thought about responses found in text‐based data (Cho & Kondoz, 2001; Donnelly et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Speech analysis of teaching assistant interventions in small group collaborative problem solving with undergraduate engineering students

D'Angelo,

Rajarathinam

2024

Brit J Educational Tech

View full text Add to dashboard Cite

This descriptive study focuses on using voice activity detection (VAD) algorithms to extract student speech data in order to better understand the collaboration of small group work and the impact of teaching assistant (TA) interventions in undergraduate engineering discussion sections. Audio data were recorded from individual students wearing head‐mounted noise‐cancelling microphones. Video data of each student group were manually coded for collaborative behaviours (eg, group task relatedness, group verbal interaction and group talk content) of students and TA–student interactions. The analysis includes information about the turn taking, overall speech duration patterns and amounts of overlapping speech observed both when TAs were intervening with groups and when they were not. We found that TAs very rarely provided explicit support regarding collaboration. Key speech metrics, such as amount of turn overlap and maximum turn duration, revealed important information about the nature of student small group discussions and TA interventions. TA interactions during small group collaboration are complex and require nuanced treatments when considering the design of supportive tools. Practitioner notesWhat is already known about this topic Student turn taking can provide information about the nature of student discussions and collaboration. Real classroom audio data of small groups typically have lots of background noise and present challenges for audio analysis. TAs have little training in how to productively intervene with students about collaborative skills. What this paper adds TA interaction with groups primarily focused on task progress and understanding of concepts with negligible explicit support on building collaborative skills. TAs intervened with the groups often which gave the students little time for uptake of their suggestions or deeper discussion. Student turn overlap was higher without the presence of TAs. Maximum turn duration can be an important real‐time turn metric to identify the least verbally active student participant in a group. Implications for practice and/or policy TA training should include information about how to monitor groups for collaborative behaviours and when and how they should intervene to provide feedback and support. TA feedback systems should keep track of previous interventions by TAs (especially in contexts where there are multiple TAs facilitating) and the duration since previous intervention to ensure that TAs do not intervene with a group too frequently with little time for student uptake. Maximum turn duration could be used as a real‐time metric to identify the least verbally active student in a group so that support could be provided to them by the TAs.

show abstract

“…As the demands for more accurate VADs in noisy conditions increase, a lot of efforts have been made to enhance the performance of VAD [2][3][4][5][6][7][8][9][10][11][12][13][14]. One successful approach is the statistical model-based VAD (SMVAD) proposed by Sohn et al [2].…”

Section: Introductionmentioning

confidence: 99%

“…More recently, various efforts have been made to optimize SMVAD by modifying the decision rule originally derived from the likelihood ratio test (LRT). To decrease detection errors at speech offset regions, Sohn et al [3] proposed an effective hang-over scheme based on the hidden Markov model (HMM), and Cho and Kondoz [4] proposed smoothed likelihood ratios (SLRs) in the decision rule. Other approaches have involved various statistical models for noise and noisy speech [5], and discriminative weight training (DWT) scheme [6].…”

Section: Introductionmentioning

confidence: 99%

Reliable likelihood ratios for statistical model-based voice activity detector with low false-alarm rate

Kim

Suh

Kim

2011

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

The role of the statistical model-based voice activity detector (SMVAD) is to detect speech regions from input signals using the statistical models of noise and noisy speech. The decision rule of SMVAD is based on the likelihood ratio test (LRT). The LRT-based decision rule may cause detection errors because of statistical properties of noise and speech signals. In this article, we first analyze the reasons why the detection errors occur and then propose two modified decision rules using reliable likelihood ratios (LRs). We also propose an effective weighting scheme considering spectral characteristics of noise and speech signals. In the experiments proposed in this study, with almost no additional computations, the proposed methods show significant performance improvement in various noise conditions. Experimental results also show that the proposed weighting scheme provides additional performance improvement over the two proposed SMVADs.

show abstract

Analysis and improvement of a statistical model-based voice activity detector

Cited by 84 publications

References 3 publications

Voice Activity Detection Based on Multiple Statistical Models

Voice Activity Detection Based on Multiple Statistical Models

Speech analysis of teaching assistant interventions in small group collaborative problem solving with undergraduate engineering students

Reliable likelihood ratios for statistical model-based voice activity detector with low false-alarm rate

Contact Info

Product

Resources

About