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- W2064429120 abstract "Although several kinds of discriminative training methods based on artificial neural networks have been vigorously tested, the pursuit of highly capable classification of variable-duration speech patterns has been unsatisfactory. In this light, the authors evaluate a generalized probabilistic descent method (GPD) in designing a speech recognizer incorporated with the dynamic time warping methodology. The algorithm can be viewed as generalized learning vector quantization suited to the dynamic programming-based time warping. Experiments were conducted on two tasks: English syllable classification and Japanese phoneme classification. Results clearly demonstrate that GPD can be a viable candidate for a method to realize a high-performance speech recognizer. >" @default.
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- W2064429120 modified "2023-09-25" @default.
- W2064429120 title "Application of a generalized probabilistic descent method to dynamic time warping-based speech recognition" @default.
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- W2064429120 doi "https://doi.org/10.1109/icassp.1992.225863" @default.
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