Patent TW-200744067-A (TW)
Title: Apparatus and method for classifying similar mandarin syllables using two consecutive Bayesian decision rules
Inventor: LI TZE-FEN
IPC: G10L15/08
Filed: 2006-05-29
Abstract: The invention uses two consecutive Bayes decision rules to classify the similar manda- rin syllables: the first Bayes decision rule is used to obtain the top M candidates {M known similar syllables) for an unknown mandarin syllable and the second Bayes decision rule is used to compute the total Bayesian distance of K training samples of each of M known similar syllables on the basis of Bayesian distance (mis-categorization risk), which are the most closest to the unknown syllable. From M total Bayesian distances, we select a similar known syllable, which has the least total Bayesian distance, to be classified as the unknown syllable. The speech waveform of a syllable is a variable length of nonlinear, time-varying ex- panded and contracted response system, even if it is uttered by the same speaker for the same syllable, which has a sequence of same speech features lined up in order in the waveform for the same syllable. The present invention discloses a pattern matching system applicable for syllable recognition which is naturally a converting means, using an uniform segmentation on the speech waveform with E equal elastic frames without filter and without overlap to stretch or contract themselves to cover the variable length of the whole speech waveform of a known syllable (the whole syllable waveform is segmented into E equal sections), for converting the waveform in the E elastic frames into a sequence of E linear predict coding (LPC) cepstra vectors such that the same j-th elastic frame, j = 1,...,E, can catch one by one in order the same LPC cepstra vector by the least squares method for the same syllable. The pattern matching system farther includes a dictionary means for storing a plurality of equal sized standard patterns of sample means and variances of linear predict coding cepstra, each equal sized (ExP) standard pattern of sample means and variances representing a known syllable. The pattern matching system further includes a converting means, using an uniform segmen- tation on the speech waveform of an unknown syllable with E equal elastic frames without filter and without overlap to stretch or contract themselves to cover the variable length of the whole speech waveform of the unknown syllable, for converting the waveform of the input unknown syllable into an equal sized (ExP) categorizing pattern of LPC cepstra vectors rep- resenting the unknown syllable, such that the same j-th frame, j=1,..., E, can catch one by one in order the same LPC cepstra vector by the least squares method for the same unknown syllable. The LPC vector is the least squares estimates which have normal distributions. The pattern matching system further includes a Bayesian categorizing means for matching the equal sized (ExP) standard pattern representing a known syllable and the equal sized (ExP) categorizing pattern representing an unknown syllable by computing a Bayesian mis-categorization risk for each of the known syllables. The Bayesian categorization means further includes a comparing and identification means for selecting M known similar syllables (top M candidates) which have the M least mis-categorization risks for the input unknown syllable. In the pattern matching system, the main invention is to use the second Bayes decision rule to compute the total Bayesian distance from the unknown syllable to the K training sam- ples of each known similar syllable which are the most closest to the unknown syllable on the basis of the Bayesian distance (mis-categorization risk). From M total Bayesian distances, the second Bayes decision rule selects a known similar syllable with the least total Bayesian distance to be the unknown syllable. Since the top M candidates for the unknown syllable has a high probability to contain the unknown syllable and using the most K accurate pronun- ciations of each known similar syllable as K different means of the similar known syllable, the second Bayes decision rule will have a high probability to detect the unknown syllable. In the pattern matching system, the invention is to use an uniform segmentation means on the waveform of a speech syllable to partition uniformly the whole waveform with E equal elastic frames without filter and without overlap to adjust themselves to cover the time-varying, nonlinear expanded and contracted speech waveform, and to produce an equal sized (ExP) pattern of E LPC cepstra vectors by the least squares method to represent a sylla- ble so that the same j-th elastic frame, j= 1, ..., E, can produce one by one in order the same LPC cepstra vector by the least squares method for the same syllable. Therefore, the catego- rizing pattern denoting an unknown syllable can be readily and immediately recognized by the pattern recognition system without further compressing or warping or adjusting the length of a sequence of LPC cepstra vectors as in the DTW matching method or in the existent pat- tern recognition methods [2]. The LPC vector (the least squares estimates) has a normal dis- tribution. The pattern matching system in the invention is established so that: (1) The pattern recognition system is naturally and theoretically derived based on the nonlinear time-varying waveforms without any arbitrary, artificial or experimental adjustments in order to improve speech recognition rate, (2) the uniform segmentation on waveform and E equal elastic frames can use the same j-th elastic frame, j= 1,..., E, to catch one by one in order the same LPC fea- ture vector for the same syllable and the different LPC feature vectors for different syllables, (3) the system can be readily and immediately used without additional training and computing for thresholds and parameters, (4) the uniform segmentation on waveform with only E=12 equal elastic frames saves the computation time in training and recognition, (5) the uniform segmentation on waveform with E=12 elastic frames without filter and without overlap significantly increases recognition rates, (6) the uniform segmentation on waveform with E equal elastic frames significantly simplifies the algorithms used in the pattern matching system, (7) the uniform segmentation on waveform with E equal elastic frames increases the applicability of the speech recognition system, i.e., the E equal elastic frames stretch or contract themselves to cover the whole speech waveform denoting a syl- lable to produce an equal sized pattern of E LPC cepstra vectors by the least squares method denoting the syllable and hence the pattern recognition system in the invention can recognize the syllable with too short or too long speech wave- forms, (8) the uniform segmentation on waveform with E equal elastic frames provides a simple feature easily extracted from speech signal waveform (9) the Bayesian categorizing means needs less time for classification on an equal sized categorizing pattern of LPC cepstra which have normal distributions and has the least mis-classification probability and hence provides higher recognition rates (10) the first Bayesian means selects the most M similar known syllables for an un- known syllable and hence the unknown syllables has high probability to be in- cluded in top N candidates and (11) using the most K accurate pronunciations of each similar known syllable as K dif- ferent means of the similar known syllable, the second Bayesian categorizing means has a high probability to detect the unknown syllable among its M similar known candidates (the second Bayes classifier uses the best K samples of a similar known syllable as K different means for the similar known syllable to match the unknown syllable).