Quality Control in Remote Speech Data Collection

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Resumé

There is a need for algorithms that can automatically control the quality of the remotely collected speech databases by detecting potential outliers which deserve further investigation.
In this paper, a simple and effective approach for identification of outliers in a speech database is proposed.
Using the deterministic minimum covariance determinant (DetMCD) algorithm to estimate the mean and covariance of the speech data in the mel-frequency cepstral domain, this approach identifies potential outliers based on the statistical distance of the observations in the feature space from the central location of the data that are larger than a predefined threshold.
The DetMCD is a computationally efficient algorithm which provides a highly robust estimate of the mean and covariance in multivariate data even when 50% of the data are outliers.
Experimental results using 8 different speech databases with manually inserted outliers show the effectiveness of the proposed method for outlier detection in speech databases. Moreover, applying the proposed method to a remotely collected Parkinson's voice database shows that the outliers that are part of the database are detected with 97.4% accuracy, resulting in significantly decreasing the effort required for manually controlling the quality of the database.
OriginalsprogEngelsk
TidsskriftIEEE Journal of Selected Topics in Signal Processing
Vol/bind13
Udgave nummer2
ISSN1932-4553
DOI
StatusUdgivet - mar. 2019

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Quality control

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    title = "Quality Control in Remote Speech Data Collection",
    abstract = "There is a need for algorithms that can automatically control the quality of the remotely collected speech databases by detecting potential outliers which deserve further investigation.In this paper, a simple and effective approach for identification of outliers in a speech database is proposed. Using the deterministic minimum covariance determinant (DetMCD) algorithm to estimate the mean and covariance of the speech data in the mel-frequency cepstral domain, this approach identifies potential outliers based on the statistical distance of the observations in the feature space from the central location of the data that are larger than a predefined threshold.The DetMCD is a computationally efficient algorithm which provides a highly robust estimate of the mean and covariance in multivariate data even when 50{\%} of the data are outliers.Experimental results using 8 different speech databases with manually inserted outliers show the effectiveness of the proposed method for outlier detection in speech databases. Moreover, applying the proposed method to a remotely collected Parkinson's voice database shows that the outliers that are part of the database are detected with 97.4{\%} accuracy, resulting in significantly decreasing the effort required for manually controlling the quality of the database.",
    keywords = "Outlier Detection, Quality Control, Robust Estimation, Speech Databases, Remote Data Collection",
    author = "Alavijeh, {Amir Hossein Poorjam} and Little, {Max A} and Jensen, {Jesper Rindom} and Christensen, {Mads Gr{\ae}sb{\o}ll}",
    year = "2019",
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    language = "English",
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    journal = "I E E E Journal on Selected Topics in Signal Processing",
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    Quality Control in Remote Speech Data Collection. / Alavijeh, Amir Hossein Poorjam; Little, Max A; Jensen, Jesper Rindom; Christensen, Mads Græsbøll.

    I: IEEE Journal of Selected Topics in Signal Processing, Bind 13, Nr. 2, 03.2019.

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

    TY - JOUR

    T1 - Quality Control in Remote Speech Data Collection

    AU - Alavijeh, Amir Hossein Poorjam

    AU - Little, Max A

    AU - Jensen, Jesper Rindom

    AU - Christensen, Mads Græsbøll

    PY - 2019/3

    Y1 - 2019/3

    N2 - There is a need for algorithms that can automatically control the quality of the remotely collected speech databases by detecting potential outliers which deserve further investigation.In this paper, a simple and effective approach for identification of outliers in a speech database is proposed. Using the deterministic minimum covariance determinant (DetMCD) algorithm to estimate the mean and covariance of the speech data in the mel-frequency cepstral domain, this approach identifies potential outliers based on the statistical distance of the observations in the feature space from the central location of the data that are larger than a predefined threshold.The DetMCD is a computationally efficient algorithm which provides a highly robust estimate of the mean and covariance in multivariate data even when 50% of the data are outliers.Experimental results using 8 different speech databases with manually inserted outliers show the effectiveness of the proposed method for outlier detection in speech databases. Moreover, applying the proposed method to a remotely collected Parkinson's voice database shows that the outliers that are part of the database are detected with 97.4% accuracy, resulting in significantly decreasing the effort required for manually controlling the quality of the database.

    AB - There is a need for algorithms that can automatically control the quality of the remotely collected speech databases by detecting potential outliers which deserve further investigation.In this paper, a simple and effective approach for identification of outliers in a speech database is proposed. Using the deterministic minimum covariance determinant (DetMCD) algorithm to estimate the mean and covariance of the speech data in the mel-frequency cepstral domain, this approach identifies potential outliers based on the statistical distance of the observations in the feature space from the central location of the data that are larger than a predefined threshold.The DetMCD is a computationally efficient algorithm which provides a highly robust estimate of the mean and covariance in multivariate data even when 50% of the data are outliers.Experimental results using 8 different speech databases with manually inserted outliers show the effectiveness of the proposed method for outlier detection in speech databases. Moreover, applying the proposed method to a remotely collected Parkinson's voice database shows that the outliers that are part of the database are detected with 97.4% accuracy, resulting in significantly decreasing the effort required for manually controlling the quality of the database.

    KW - Outlier Detection

    KW - Quality Control

    KW - Robust Estimation

    KW - Speech Databases

    KW - Remote Data Collection

    U2 - 10.1109/JSTSP.2019.2904212

    DO - 10.1109/JSTSP.2019.2904212

    M3 - Journal article

    VL - 13

    JO - I E E E Journal on Selected Topics in Signal Processing

    JF - I E E E Journal on Selected Topics in Signal Processing

    SN - 1932-4553

    IS - 2

    ER -