Application of a generalized probabilistic descent method to dynamic time warping-based speech recognition
Takashi Komori, Shigeru Katagiri · 1992
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.>