Fusion of handwritten word classifiers
Pattern Recognition Letters - Special issue on fuzzy set technology in pattern recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multistage classifiers optimized by neural networks and genetic algorithms
Proceedings of the second world congress on Nonlinear analysts: part 3
Soft combination of neural classifiers: a comparative study
Pattern Recognition Letters
Automatic ToBI prediction and alignment to speed manual labeling of prosody
Speech Communication - Special issue on speech annotation and corpus tools
Practical Applications of Fuzzy Technologies
Practical Applications of Fuzzy Technologies
Improved Pairwise Coupling Classification with Correcting Classifiers
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Automatic corpus-based tone and break-index prediction using K-ToBI representation
ACM Transactions on Asian Language Information Processing (TALIP)
Tree Induction for Probability-Based Ranking
Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Applying data mining techniques to corpus based prosodic modeling
Speech Communication
Intonation modeling for Indian languages
Computer Speech and Language
Automatic prosodic events detection using syllable-based acoustic and syntactic features
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Detecting pitch accents at the word, syllable and vowel level
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Advanced soft computing diagnosis method for tumour grading
Artificial Intelligence in Medicine
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Fuzzy-input fuzzy-output one-against-all support vector machines
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part III
Genetic fuzzy classifier for sleep stage identification
Computers in Biology and Medicine
Classification of prosodic events using Quantized Contour Modeling
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
From English pitch accent detection to Mandarin stress detection, where is the difference?
Computer Speech and Language
Analysis of inter-transcriber consistency in the Cat_ToBI prosodic labeling system
Speech Communication
Automatic Prosodic Event Detection Using Acoustic, Lexical, and Syntactic Evidence
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Audio, Speech, and Language Processing
Use of fuzzy-logic-inspired features to improve bacterialrecognition through classifier fusion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
"Fuzzy" versus "nonfuzzy" in combining classifiers designed by Boosting
IEEE Transactions on Fuzzy Systems
Multiple network fusion using fuzzy logic
IEEE Transactions on Neural Networks
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This paper presents an original approach to automatic prosodic labeling. Fuzzy logic techniques are used for representing situations of high uncertainty with respect to the category to be assigned to a given prosodic unit. The Fuzzy Integer technique is used to combine the output of different base classifiers. The resulting fuzzy classifier benefits from the different capabilities of the base classifiers for identifying different types of prosodic events. At the same time, the fuzzy classifier identifies the events that are potentially more difficult to be labeled. The classifier has been applied to the identification of ToBI pitch accents. The state of the art on pitch accent multiclass classification reports around 70% accuracy rate. In this paper we describe a fuzzy classifier which assigns more than one label in confusing situations. We show that the pairs of labels that appear in these uncertain situations are consistent with the most confused pairs of labels reported in manual prosodic labeling experiments. Our fuzzy classifier obtains a soft classification rate of 81.8%, which supports the potential of the proposed system for computer assisted prosodic labeling.