Patent ID: 8145483
Filing Date: 2012-03-27
Classification: G10L

Abstract:
1. A speech recognition method for all languages without using samples provides speech recognition on all languages comprising: (1). providing a raw database of unknown words or unknown continuous voices with having two or more samples of any languages and a raw database of unknown words or unknown voices having only words or voices without samples of any languages; (2). providing a permanent database of known words pronounced by a speaker with a standard, clear and distinct utterance or by the testing speaker; (3). deleting noise and the time interval without speech signal by using a processor; (4). normalizing the whole speech waveform of a word or a continuous voice, and transforming the waveform into an equal-sized E×P=12×12 matrix of the linear predictive coding cepstra by using E=12 elastic frames without filter and without overlap; (5). computing the sample means and sample variances of linear predict coding cepstra from the samples of each unknown word or each unknown continuous voice in the raw database having two or more samples; (6). finding the top N unknown words by using Bayesian distances from the raw database of unknown words or voices having two or more samples with sample means and variances with the N shortest Bayesian distances to the known word in the permanent database and finding the top N unknown words or voices in the raw database of unknown words or voices having only words or voices without samples with the N shortest absolute distances to the known word in the permanent database; (7). computing the weighted average from N+1 data of the N sample means of the top N unknown words in the raw database of unknown words having two or more samples with sample means and variances with the N shortest Bayesian distances to the known word and one linear predictive coding cepstra of the known word in the permanent database as the mean for the known word, computing the weighted average from N data of the N sample variances of the top N unknown words as the variance for the known word for each known word in the permanent database, denoting the E×P=12×12 matrix of the means and the variances of the known word as the feature of the known word, called the standard pattern of the known word and saving the standard pattern of the known word with other known words of several different languages into the word database and making necessary sentences or names from front the known words in the word database to be saved into the sentence and name database; (8). if the unknown words or voices in the raw database do not have samples, considering the N linear predictive coding cepstra (LPCC) of the top N unknown words in the raw database with the N shortest distances to the known word and the LPCC of the known word in the permanent database as (N+1) data, computing the mean (weighted average) and the variance of (N+1) data and saving the E×P=12×12 matrix of the means and the variances, called the feature of the known word as the standard pattern, into the word database; (9). normalizing the whole waveform of an input unknown word by using E=12 elastic frames without filter and without overlap and transforming the whole waveform into an equal-sized E×P=12×12 matrix of linear predictive coding cepstra, called the categorizing pattern of the unknown word; (10). matching the standard pattern of every known word in the word database with the categorizing pattern of an input unknown word and finding a known word in the word database with the least Bayesian distance to the unknown word to be the unknown word by using a simplified Bayesian decision classifier; (11). partitioning an unknown sentence or name into D unknown words; (12). finding the top F similar known words from the word database for each unknown word by the Bayesian classifier, and representing an unknown sentence or name by a D×F matrix of known similar words of several languages; (13). matching the D×F matrix of similar known words denoting an unknown sentence or name with all known sentences and names in the sentence and name database and finding a known sentence or name in the sentence and name database with the highest probability to be the unknown sentence or name; (14). improving the feature of an unknown word in the input unknown sentence or name such that the input unknown word sentence or name is guaranteed to be recognized correctly.