Hierarchical partition of the articulatory state space for overlapping-feature based speech recognition
نویسندگان
چکیده
We describe our recent work on improving an overlapping articulatory feature (sub-phonemic) based speech recognizer with robustness to the requirement of training data. A new decision-tree algorithm is developed and applied to the recognizer design which results in hierarchical partitioning of the articulatory state space. The articulatory states associated with common acoustic correlates, a phenomenon caused by the many-tosne articulation-to-acoustics mapping well known in speech production, are automatically clustered by the decision-tree algorithm. This enables effective prediction of the unseen articulatory states in the training, thereby increasing the recognizer’s robusmess. Some preliminary experimental results are provided.
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