Fuzzy-Clustering-Based Decision Tree Approach for Large Population Speaker Identification

Adaptive decision tree-based phone cluster models for speaker clustering

This study presents an approach to speaker clustering using adaptive decision tree-based phone cluster models (DT-PCMs). First, a large broadcast news database is used to train a set of phone models for universal speakers. The multi-space probability distributed-hidden Markov model (MSD-HMM) is adopted for phone modeling. Confusing phone models are merged into phone clusters. Next, for each sta...

Fuzzy Decision Tree Induction Approach for Mining Fuzzy Association Rules

Decision Tree Induction (DTI), one of the Data Mining classification methods, is used in this research for predictive problem solving in analyzing patient medical track records. In this paper, we extend the concept of DTI dealing with meaningful fuzzy labels in order to express human knowledge for mining fuzzy association rules. Meaningful fuzzy labels (using fuzzy sets) can be defined for each...

Fuzzy C-Means Clustering-Based Speaker Verification

In speaker verification, a claimed speaker’s score is computed to accept or reject the speaker claim. Most of the current normalisation methods compute the score as the ratio of the claimed speaker’s and the impostors’ likelihood functions. Based on analysing false acceptance error occured by the current methods, we propose a fuzzy c-means clusteringbased normalisation method to find a better s...

Decision Tree Based Fuzzy Reasoning

Fuzzy logic techniques are efficient in solving complex, ill-defined problems that are characterized by uncertainty of environment and fuzziness of information. Fuzzy logic allows handling uncertain and imprecise knowledge and provides a powerful framework for reasoning. Fuzzy reasoning models are relevant to a wide variety of subject areas such as engineering, economics, psychology, sociology,...

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