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Enhancing Eigenspace-Based MLLR Speaker Adaptation Using a Fuzzy Logic Learning Control Scheme
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2011/10/01
Online ISSN: 1745-1361
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Section on Information-Based Induction Sciences and Machine Learning)
speech recognition, speaker adaptation, HMM, Eigen-MLLR, fuzzy control,
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This study develops a fuzzy logic control mechanism in eigenspace-based MLLR speaker adaptation. Specifically, this mechanism can determine hidden Markov model parameters to enhance overall recognition performance despite ordinary or adverse conditions in both training and operating stages. The proposed mechanism regulates the influence of eigenspace-based MLLR adaptation given insufficient training data from a new speaker. This mechanism accounts for the amount of adaptation data available in transformation matrix parameter smoothing, and thus ensures the robustness of eigenspace-based MLLR adaptation against data scarcity. The proposed adaptive learning mechanism is computationally inexpensive. Experimental results show that eigenspace-based MLLR adaptation with fuzzy control outperforms conventional eigenspace-based MLLR, and especially when the adaptation data acquired from a new speaker is insufficient.