Quality Control in Remote Speech Data Collection

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Abstract

There is the 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.
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There is the 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.
Original languageEnglish
JournalIEEE Journal of Selected Topics in Signal Processing
ISSN1932-4553
Publication statusAccepted/In press - 2019
Publication categoryResearch
Peer-reviewedYes

    Research areas

  • Outlier Detection, Quality Control, Robust Estimation, Speech Databases, Remote Data Collection
ID: 294939308