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P.h. Holmes - One of the best experts on this subject based on the ideXlab platform.

  • A voice activity detector using the Chi-Square Test
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: B. Ahmed, P.h. Holmes
    Abstract:

    This paper proposes a voice activity detector (VAD) that makes the speech/noise classification by applying the statistical Chi-Square Test to each frame. It also uses a continuous update of the background noise estimate. The speech is first enhanced using a noise reduction system, with noise estimates also obtained with the help of the Chi-Square Test. The noise-reduced signal is decomposed into sub-bands, and the Chi-Square Test is used again in another form to compare the observed signal distribution to the estimated noise distribution. If the Chi-Square Test determines that they are close, the frame is declared to be noise, otherwise speech. The performance of this VAD was found to be significantly superior to several benchmark VAD, with accuracies above 89% even at a SNR of 0 dB, which is up to 25% better than the others.

  • ICASSP (1) - A voice activity detector using the Chi-Square Test
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: B. Ahmed, P.h. Holmes
    Abstract:

    This paper proposes a voice activity detector (VAD) that makes the speech/noise classification by applying the statistical Chi-Square Test to each frame. It also uses a continuous update of the background noise estimate. The speech is first enhanced using a noise reduction system, with noise estimates also obtained with the help of the Chi-Square Test. The noise-reduced signal is decomposed into sub-bands, and the Chi-Square Test is used again in another form to compare the observed signal distribution to the estimated noise distribution. If the Chi-Square Test determines that they are close, the frame is declared to be noise, otherwise speech. The performance of this VAD was found to be significantly superior to several benchmark VAD, with accuracies above 89% even at a SNR of 0 dB, which is up to 25% better than the others.

B. Ahmed - One of the best experts on this subject based on the ideXlab platform.

  • A voice activity detector using the Chi-Square Test
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 2004
    Co-Authors: B. Ahmed, P.h. Holmes
    Abstract:

    This paper proposes a voice activity detector (VAD) that makes the speech/noise classification by applying the statistical Chi-Square Test to each frame. It also uses a continuous update of the background noise estimate. The speech is first enhanced using a noise reduction system, with noise estimates also obtained with the help of the Chi-Square Test. The noise-reduced signal is decomposed into sub-bands, and the Chi-Square Test is used again in another form to compare the observed signal distribution to the estimated noise distribution. If the Chi-Square Test determines that they are close, the frame is declared to be noise, otherwise speech. The performance of this VAD was found to be significantly superior to several benchmark VAD, with accuracies above 89% even at a SNR of 0 dB, which is up to 25% better than the others.

  • ICASSP (1) - A voice activity detector using the Chi-Square Test
    2004 IEEE International Conference on Acoustics Speech and Signal Processing, 1
    Co-Authors: B. Ahmed, P.h. Holmes
    Abstract:

    This paper proposes a voice activity detector (VAD) that makes the speech/noise classification by applying the statistical Chi-Square Test to each frame. It also uses a continuous update of the background noise estimate. The speech is first enhanced using a noise reduction system, with noise estimates also obtained with the help of the Chi-Square Test. The noise-reduced signal is decomposed into sub-bands, and the Chi-Square Test is used again in another form to compare the observed signal distribution to the estimated noise distribution. If the Chi-Square Test determines that they are close, the frame is declared to be noise, otherwise speech. The performance of this VAD was found to be significantly superior to several benchmark VAD, with accuracies above 89% even at a SNR of 0 dB, which is up to 25% better than the others.

C Schwertmanneil - One of the best experts on this subject based on the ideXlab platform.

Neil C Schwertman - One of the best experts on this subject based on the ideXlab platform.

Peter M Bentler - One of the best experts on this subject based on the ideXlab platform.

  • a scaled difference Chi Square Test statistic for moment structure analysis
    Department of Statistics UCLA, 2011
    Co-Authors: Albert Satorra, Peter M Bentler
    Abstract:

    A Scaled Di erence Chi-Square Test Statistic for Moment Structure Analysis Albert Satorra Universitat Pompeu Fabra and Peter M. Bentler University of California, Los Angeles August 3, 1999 Research supported by the Spanish DGES grant PB96-0300, and USPHS grants DA00017 and DA01070.

  • ensuring positiveness of the scaled difference Chi Square Test statistic
    Psychometrika, 2010
    Co-Authors: Albert Satorra, Peter M Bentler
    Abstract:

    A scaled difference Test statistic \(\tilde{T}{}_{d}\) that can be computed from standard software of structural equation models (SEM) by hand calculations was proposed in Satorra and Bentler (Psychometrika 66:507–514, 2001). The statistic \(\tilde{T}_{d}\) is asymptotically equivalent to the scaled difference Test statistic \(\bar{T}_{d}\) introduced in Satorra (Innovations in Multivariate Statistical Analysis: A Festschrift for Heinz Neudecker, pp. 233–247, 2000), which requires more involved computations beyond standard output of SEM software. The Test statistic \(\tilde{T}_{d}\) has been widely used in practice, but in some applications it is negative due to negativity of its associated scaling correction. Using the implicit function theorem, this note develops an improved scaling correction leading to a new scaled difference statistic \(\bar{T}_{d}\) that avoids negative Chi-Square values.

  • ensuring positiveness of the scaled difference Chi Square Test statistic
    Department of Statistics UCLA, 2008
    Co-Authors: Albert Satorra, Peter M Bentler
    Abstract:

    A scaled difference Test statistic T_tildad that can be computed from standard software of structural equation models (SEM) by hand calculations was proposed in Satorra and Bentler (2001). The statistic T_tildad is asymptotically equivalent to the scaled difference Test statistic T_hatd introduced in Satorra (2000), which requires more involved computations beyond standard output of SEM software. The Test statistic T_tildad has been widely used in practice, but in some applications it is negative due to negativity of its associated scaling correction. Using the implicit function theorem, this note develops an improved scaling correction leading to a new scaled difference statistic T_hatd that avoids negative Chi-Square values.

  • a scaled difference Chi Square Test statistic for moment structure analysis
    Psychometrika, 2001
    Co-Authors: Albert Satorra, Peter M Bentler
    Abstract:

    A family of scaling corrections aimed to improve the Chi-Square approximation of goodness-of-fit Test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, Satorra-Bentler's (SB) scaling corrections are available in standard computer software. Often, however, the interest is not on the overall fit of a model, but on a Test of the restrictions that a null model sayM 0 implies on a less restricted oneM 1. IfT 0 andT 1 denote the goodness-of-fit Test statistics associated toM 0 andM 1, respectively, then typically the differenceT d =T 0−T 1 is used as a Chi-Square Test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the modelsM 0 andM 1. As in the case of the goodness-of-fit Test, it is of interest to scale the statisticT d in order to improve its Chi-Square approximation in realistic, that is, nonasymptotic and nonormal, applications. In a recent paper, Satorra (2000) shows that the difference between two SB scaled Test statistics for overall model fit does not yield the correct SB scaled difference Test statistic. Satorra developed an expression that permits scaling the difference Test statistic, but his formula has some practical limitations, since it requires heavy computations that are not available in standard computer software. The purpose of the present paper is to provide an easy way to compute the scaled difference Chi-Square statistic from the scaled goodness-of-fit Test statistics of modelsM 0 andM 1. A Monte Carlo study is provided to illustrate the performance of the competing statistics.

  • a scaled difference Chi Square Test statistic for moment structure analysis
    Department of Statistics UCLA, 1999
    Co-Authors: Albert Satorra, Peter M Bentler
    Abstract:

    A family of scaling corrections aimed to improve the Chi-Square approximation of goodness-of-fit Test statistics in small samples, large models, and nonnormal data was proposed in Satorra and Bentler (1994). For structural equations models, Satorra-Bentler's (SB) scaling corrections are available in standard computer software. Often, however, the interest is not on the overall fit of a model, but on a Test of the restrictions that a null model say ${\cal M}_0$ implies on a less restricted one ${\cal M}_1$. If $T_0$ and $T_1$ denote the goodness-of-fit Test statistics associated to ${\cal M}_0$ and ${\cal M}_1$, respectively, then typically the difference $T_d = T_0 - T_1$ is used as a Chi-Square Test statistic with degrees of freedom equal to the difference on the number of independent parameters estimated under the models ${\cal M}_0$ and ${\cal M}_1$. As in the case of the goodness-of-fit Test, it is of interest to scale the statistic $T_d$ in order to improve its Chi-Square approximation in realistic, i.e., nonasymptotic and nonnormal, applications. In a recent paper, Satorra (1999) shows that the difference between two Satorra- Bentler scaled Test statistics for overall model fit does not yield the correct SB scaled difference Test statistic. Satorra developed an expression that permits scaling the difference Test statistic, but his formula has some practical limitations, since it requires heavy computations that are not available in standard computer software. The purpose of the present paper is to provide an easy way to compute the scaled difference Chi-Square statistic from the scaled goodness-of-fit Test statistics of models ${\cal M}_0$ and ${\cal M}_1$. A Monte Carlo study is provided to illustrate the performance of the competing statistics.