Calibrating AdaBoost for phoneme classification

DOI10.1007/s00500-018-3577-zzbMath1415.68181OpenAlexW2898269002WikidataQ129027975 ScholiaQ129027975MaRDI QIDQ2001122

Róbert Busa-Fekete, Gábor Gosztolya

Publication date: 2 July 2019

Published in: Soft Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00500-018-3577-z

zbMATH Keywords

speech recognitionphoneme classificationAdaBoost.MHphoneme probability estimationposterior calibration

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Natural language processing (68T50)

Uses Software

• MultiBoost
• AdaBoost.MH

Cites Work

• Additive logistic regression: a statistical view of boosting. (With discussion and a rejoinder by the authors)
• Improved boosting algorithms using confidence-rated predictions
• Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers
• Estimating the Support of a High-Dimensional Distribution
• 10.1162/15324430260185628
• An Empirical Distribution Function for Sampling with Incomplete Information
• Introduction to Information Retrieval
• Improved QR Code Localization Using Boosted Cascade of Weak Classifiers
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