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Using Extreme Gradient Boosting to Detect Glottal Closure Instants in Speech Signal | IEEE Conference Publication | IEEE Xplore

Using Extreme Gradient Boosting to Detect Glottal Closure Instants in Speech Signal


Abstract:

In this paper, we continue to investigate the use of classifiers for the automatic detection of glottal closure instants (GCIs) from the speech signal. We focus on extrem...Show More

Abstract:

In this paper, we continue to investigate the use of classifiers for the automatic detection of glottal closure instants (GCIs) from the speech signal. We focus on extreme gradient boosting (XGB), a fast and powerful implementation of a gradient boosting algorithm. We show that XGB outperforms other classifiers, achieving GCI detection accuracy F 1 = 98.55% and AUC = 99.90%. The proposed XGB model is also shown to outperform other existing GCI detection algorithms on publicly available databases. Despite using much less training data, the performance of XGB is comparable to a deep convolutional neural network based approach, especially when it is tested on voices that were not included in the training data.
Date of Conference: 12-17 May 2019
Date Added to IEEE Xplore: 17 April 2019
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Conference Location: Brighton, UK

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