One-Sample Speech Recognition of Mandarin Monosyllables using Unsupervised Learning

One-Sample Speech Recognition of Mandarin Monosyllables using Unsupervised Learning

In the speech recognition, a mandarin syllable wave is compressed into a matrix of linear predict coding cepstra (LPCC), i.e., a matrix of LPCC represents a mandarin syllable. We use the Bayes decision rule on the matrix to identify a mandarin syllable. Suppose that there are K di erent mandarin syllables, i.e., K classes. In the pattern classi cation problem, it is known that the Bayes decision rule, which separates K classes, gives a minimum probability of misclassi cation. In this study, a set of unknown syllables is used to learn all unknown parameters (means and variances) for each class. At the same time, in each class, we need one known sample (syllable) to identify its own means and variances among K classes. Finally, the Bayes decision rule classi es the set of unknown syllables and input unknown syllables. It is an one-sample speech recognition. This classi er can adapt itself to a better decision rule by making use of new unknown input syllables while the recognition system is put in use. In the speech experiment using unsupervised learning to nd the unknown parameters, the digit recognition rate is improved by 22%.