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phoneme recognition using boosted binary features
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由 A Roy 著作被引用 12 次 — ABSTRACT. In this paper, we propose a novel parts-based binary-valued feature for ASR. This feature is extracted using boosted ensembles of simple.
(PDF) Phoneme recognition using Boosted Binary Features
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2024年10月22日 — Preliminary studies on TIMIT phoneme recognition task show that BBF yields similar or better performance compared to MFCC (67.8% accuracy for ...
Continuous Speech Recognition using Boosted Binary Features
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Previous studies on TIMIT phoneme recognition task showed that BBF yields similar or better performance compared to cepstral features. In this work, this study ...
Boosting localized binary features for speech recognition
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由 A Roy 著作2012被引用 2 次 — The features were found to perform significantly better (when coupled with SLP) and equally well (when coupled with MLP) compared to MFCC features on the TIMIT ...
Phoneme recognition using Boosted Binary Features | CiNii Research
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Phoneme recognition using Boosted Binary Features. DOI PDF 被引用文献1件. Anindya Roy · Mathew Magimai.-Doss · Sebastien Marcel. 収録刊行物. 2011 IEEE ...
Continuous Speech Recognition using Boosted Binary Features
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This study is extended to continuous speech recognition task on the DARPA Resource Management database and shows that BBF achieves comparable word error ...
(PDF) Boosting Localized Binary Features for Speech Recognition
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PDF | On Jan 1, 2012, A Roy and others published Boosting Localized Binary Features for Speech Recognition | Find, read and cite all the research you need ...
Boosting Localized Binary Features for Speech Recognition
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由 A Roy 著作被引用 2 次 — Previously, phoneme recognition studies on the TIMIT database [1] showed that BBF achieved a phoneme recog- nition rate of 67.8% which is slightly better than ...
[PDF] Boosting localized binary features for speech recognition
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The current work presents an overview of BBF with MFCC and an analysis of their complementarity, and scalability of the proposed features from phoneme ...
Feature Selection Using Adaboost for Phoneme Recognition
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Abstract The propose to improve a Support Vector Machines (SVM) learning accuracy by using a Real Adaboost algorithm for selecting features is presented.