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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  • Quality Control in Remote Speech Data Collection

    Rights statement: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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Details

Original languageEnglish
Pages (from-to)1-1
JournalIEEE Journal on Selected Topics in Signal Processing
Early online date11 Mar 2019
DOIs
Publication statusE-pub ahead of print - 11 Mar 2019

Bibliographic note

© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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