Data-derived Nonlinear Mapping for Feature Extraction in Hmm
نویسندگان
چکیده
Rather long temporal trajectory of critical band logarithmic power spectrum energy at a given frequency is used as an input feature vector in a MLP-based phoneme classi er, trained on a task-independent hand-labeled development data. Class-speci c log likelihood vectors from the individual sub-classi ers form input to a merging MLP classi er trained on the training data. Output of this merging classi er forms a feature vector for subsequent HMM ASR.
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تاریخ انتشار 1999