Patent Document ID: 8160866
Application ID: 12248924

Base Claim:
1. A speech recognition method for both English and Chinese provides both English word, sentence, name, and Chinese syllable, sentence, name recognition comprising: (1). providing an English word and Chinese syllable database, a English and Chinese sentence and name database and deleting noise and the time interval without speech signal by using a processor; (2). normalizing the whole speech waveform of a known English word (Chinese syllable) by using E=12 elastic frames without filter and without overlap and transforming the waveform into an E×P=12×12 matrix of linear predictive coding cepstra (LPCC) such that the same English words (Chinese syllables) have about the same LPCC at the same time position in their E×P=12×12 matrices of LPCC; (3). wherein from all samples of each known English word (Chinese syllable), select its best K samples to compute the feature to represent the known English word (Chinese syllable); (4). wherein the best K samples of a known English word (Chinese syllable) are used to compute the means and all samples of the known English word (Chinese syllable) are used to compute the variances of LPCC and the E×P=12×12 matrix of means and variances is called the standard pattern to represent the feature of the known English word (Chinese syllable) and is saved in the English word and Chinese syllable database; (5). normalizing the whole waveform of an input unknown English word (Chinese syllable) by using E=12 elastic frames without filter and without overlap and transforming the waveform into an E×P=12×12 matrix of linear predict coding cepstra (LPCC), which is called the categorizing pattern to represent the unknown English word (Chinese syllable); (6). wherein a simplified Bayesian decision classifier is used to match the standard pattern of every known English word (Chinese syllable) with the categorizing pattern of an input unknown English word (Chinese syllable) and to find a known English word (Chinese syllable) with the least Bayesian distance to be the unknown English word (Chinese syllable); (7). modifying and improving the feature of an English word (Chinese syllable) such that the English word (Chinese syllable) is guaranteed to be recognized correctly, and creating the feature (standard pattern) of a new English word and a new Chinese syllable; (8). partitioning an unknown English (Chinese) sentence or name into D unknown English words (D unknown Chinese syllables); (9). wherein a simplified Bayesian classifier to find the F most similar known English words or Chinese syllables or both from the English word and Chinese syllable database for each of D unknown English words (Chinese syllables) and representing an unknown English (Chinese) sentence or name by using a D×F matrix of known similar English words or Chinese syllables or both; (10). wherein the D×F matrix of similar known English words (Chinese syllables) is used to matches all known English (Chinese) sentences and names in the English and Chinese sentence and name database and find a known English (Chinese) sentence or name with the highest probability to be the unknown sentence or name; (11). modifying and improving the feature of an unknown English word (Chinese syllable) in the input unknown English (Chinese) sentence or name such that the input unknown sentence or name is guaranteed to be recognized correctly; and (12). since the features of English words (without samples) are created from the features of Chinese syllables, performing speech recognition on other languages, such as German, French, Japanese, Korean, and Russian, wherein the features of said other languages can be created from the features of Chinese syllables to perform said speech recognition.

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Claim 5:
5. The speech recognition method for both English and Chinese as claimed in claim 1 , wherein said step further includes transforming the waveform of an unknown English word (Chinese syllable) into an E×P=12×12 matrix of LPCC: (a). partitioning the whole waveform of the unknown English word (Chinese syllable) into E=12 equal sections and forming each section as an elastic frame without filter and without overlap; (b). using a linear regression model in each elastic frame to estimate the nonlinear, time-varying waveform and producing P regression coefficients by the lease squares method; (c). implementing the least squares method by using Durbin's recursive equations R ⁡ ( i ) = ∑ n = 0 N - i ⁢ S ⁡ ( n ) ⁢ S ⁡ ( n + i ) , i ≥ 0 E 0 = R ⁡ ( 0 ) k i = [ R ⁡ ( i ) - ∑ j = 1 i - 1 ⁢ a j ( i - 1 ) ⁢ R ⁡ ( i - j ) ] / E i - 1 a i ( i ) = k i a j ( i ) = a j ( i - 1 ) - k i ⁢ a i - j ( i - 1 ) , 1 ≤ j ≤ i - 1 E i = ( 1 - k i 2 ) ⁢ E i - 1 a j = a j ( P ) , 1 ≤ j ≤ P to compute the least squares estimate vector, called a (LPC vector) a j , j=1, . . . , P; (d). transforming the LPC vector into a more stable LPC cepstra (LPCC) vector â i , i=1, . . . ,P, by the equations a ^ i = a i + ∑ j = 1 i - 1 ⁢ ⁢ ( j i ) ⁢ a i - j ⁢ a ^ j , 1 ≤ i ≤ P a ^ i = ∑ j = i - P i - 1 ⁢ ( j i ) ⁢ a i - j ⁢ a ^ j , P < i ; (e). calling the E×P=12×12 matrix of LPCC as the categorizing pattern to represent the feature of the unknown English word (Chinese syllable).