TW200935399A - Chinese-speech phonologic transformation system and method thereof - Google Patents

Abstract

The invention discloses a Chinese-speech phonologic transformation system, which includes the following: a phonologic analysis unit used to receive a source speech and corresponding source characters to generate a synthetic speech, wherein the phonologic analysis unit comprises a hierarchy deconstructing module used to define the source characters by plural hierarchies, each hierarchy having one pitch model, and to generate phonologic parameters of the pitch model according to the source speech; a function selection module determining individual phonologic transformation function according to the pitch model and the corresponding phonologic parameters; a phonologic transformation module carrying out the phonologic transformation functions to respectively transform individual phonologic parameter into individual synthetic phonologic parameters; and a synthesis module executing corresponding pitch models according to individual synthetic phonologic parameter to generate the synthetic speech. The invention also discloses a Chinese-speech phonologic transformation method.

Description

200935399 IX. INSTRUCTIONS: [Technical Field] The present invention relates to a Chinese speech and rhyme conversion system and method, and more particularly to a Chinese speech and rhyme conversion system and method for computer synthesized speech. [Prior technology] Voice technology is an important key to the development of computer technology for the next generation. With the maturity of network, control system and pattern recognition technology, smart home and medical service systems have entered the stage of application development. . However, all services will eventually need to go back to the users themselves. The development of human-computer interaction has become the key to practical applications. </ br> The voice technology is the interface between the user and the computer. Voice technology mainly includes two technologies: speech recognition and speech synthesis. The speech recognition mainly analyzes the speech generated by the user by the computer and confirms the instructions contained in the speech. The speech synthesis mainly generates the approximation through the computer in a digital manner. The output voice of the human voice. Among them, the unitary-style speech synthesis system that selects and concatenates through units can synthesize quite natural computer speech, and even rich emotional speech with different emotional changes of different speakers. Voice synthesis technology (Voice Conversion (VC) technology used in the past is mostly used as a tool for speaker conversion. Most technologies need to record so-called parallel corpora, establish source language and After the correspondence of the target language, the conversion model is trained and then converted. Among them, in the conversion part of the phonology, it can roughly distinguish between Unear Mapping, lining (vect〇r卩), Gaussian Mixture M〇del (GMM), and classification. The Classification and Regression Tree (CART) ° 200935399 In the linear matching part, the pitch average and the number of variances obtained from the source speech and the target speech in the training corpus are used for linear estimation. This method is generally the most simplified way, so the rhythm obtained after the conversion cannot satisfy the general statement. At different positions, the accent and pitch rise and fall. For example, the same word at the beginning, middle or end of a sentence, its accent changes, even in the same way, will have different presentations. In the vector quantization part, the obtained speech feature vector of the source speech and the target speech in the training corpus is calibrated to establish a corresponding codebook '^odebook)' and each codeword in the codebook ((3) and Li... are the corresponding target speech feature vectors. When converting, the codeword corresponding to the feature vector of the source speech is first obtained, and the speech feature vector of the target speech is replaced by the target character to be restored to the vertical &amp; - Shi, ... person? Sound output. This method is simple, turn, 佬S temporarily 不 种 种 discontinuous conversion results, limited conversion effect so that the sound quality is also limited. ~ ~ ~ Da Li injury 'its basic The hypothesis is the source speech and the target direction, and the probability distribution is

Distribution), derived from this - V ° mal ❹ used in the conversion of the spectrum, (four) material ° this _ change method is first in the Gaussian mixture model part of the main 4 =: =:: rhyme part. Vector, then the GMM training two law parameters, " becomes a characteristic change between the spectrum and the pitch: but, the combination, the formula. This method takes into account the problem 'and produces a discontinuous phenomenon, for the frame gamma Me) The correlation between the calibration and the rhythm 'has the quality of the sound, and the spectrum also has some separate analysis of the rhythm of the spectrum. In addition, the rhyme model training, this method is indeed the early το GMM The sound is also limited by the definition of the conversion model = the discontinuity of the phenomenon is produced, like the image. The phonology that is transferred out of the piece has been homogenized. 200935399 In the classification and regression tree part, unlike GMM only Lixun does Considering that CART combines the part of linguistics, the relationship between the words is a criterion for consideration, and it is expected to establish a more powerful transformation model. However, studies have shown that when training CART, the corresponding training corpus should be A larger amount can make the established conversion model accurate. However, the collection of the language ^ has always been the key to the development of this technology, for a large number of voice corpus is the bottleneck of the practical application of voice technology. The object of the present invention is to provide a Chinese speech and rhyme conversion system and method capable of achieving accurate phonological conversion under the requirement of reducing corpus. To achieve the above object, the present invention provides a Chinese speech phonological conversion system having a The phonological analysis unit is configured to receive a source speech and a corresponding source text and generate a synthesized speech, the phonological analysis unit comprising: a hierarchical disassembly module defining the source text by a plurality of levels, each level having a two-pitch model And generating a phoneme parameter of the pitch model according to the source speech; a function selection module 'determining the respective phoneme conversion functions according to the pitch model and the corresponding phoneme parameter; a phoneme conversion module, executing the The phonological conversion function converts the respective phonological parameters into respective synthetic phonological parameters, and a synthesis module that performs a corresponding pitch model according to the respective synthesized phonological parameters to generate the synthesized speech. The present invention provides a Chinese voice and rhyme conversion method for receiving a source voice and Generating the text and generating a synthesized speech' includes: generating a plurality of pitch models according to the source text; generating pitch parameters of each pitch model according to the source speech; determining according to the pitch models and corresponding pitch parameters Respective phonological conversion functions; performing the phonological conversion functions to convert respective phonological parameters into respective synthesized phonological parameters; and performing corresponding pitch models according to respective synthesized 200935399 phonological parameters to generate the synthesized speech. In summary, the Chinese speech sound and rhyme conversion system and method of the present invention can be applied to various human-machine single/two-way communication systems, wherein the hierarchical phonetic model=only conforms to the phonetic structure on the human voice, and can reduce the pitch. The degree of variation of the parameters; coupled with the regression clustering algorithm, effectively reduces the prediction error on the parameter conversion, making the phonological conversion more accurate, and the computer speech synthesis system applied to multi-linguistics or rich emotions can reduce the demand for sound corpus. The above-mentioned objects and features of the present invention will be described in detail with reference to the accompanying drawings. The present invention will be fully described with reference to the accompanying drawings in which the preferred embodiments of the present invention are described, but it is understood that those skilled in the art can modify the invention described herein while obtaining the present invention. The efficacy of the invention. Therefore, the following description is to be understood by those skilled in the art and is not intended to limit the invention. The invention is a Chinese phonetic rhyme conversion system, which mainly comprises a hierarchical phonetic analysis structure, a regression grouping algorithm and a phoneme conversion function based on the Classification and Regression Tree (CART). Selection mechanism. The Chinese phonetic rhyme conversion system inputs arbitrary Chinese phonetic sentences and corresponding text content and syllable breakpoints, and the hierarchical phoneme analysis structure decomposes the phonemes into the phonetic parameters of the sentence class, the word class and the sub-syllable class. The training of the phoneme conversion function is through the regression grouping algorithm, and the prediction error of the phonetic parameters and the linguistic feature similarity are considered. Based on the classification regression &amp; CART, the selection of the conversion function of the phonological reference (four) in the Xiang language and the source speech. Then according to the selection of the lyrics (four), the phonological parameters of each class are divided into 8 200935399 g lines, which will be converted. The phonological parameters are combined to synthesize the converted synthesized sounds to mention the phonology, and generally include the rhythm of the entire sentence, which is the J-sex of the pitch, the most direct influence and the composition of the sentence, and the sentence category. The "limitation" may also come from phonological changes, such as: :== habits affecting 'causing these various factors, the six rhythm conversion functions of the invention are parallel training corpus, based In the embodiment of the present invention, the frequency; 曰, the commutative function is also established at the same time to process the conversion of the spectrum. The input sentence requires a text!! The hierarchical rhythm model is disassembled and/or transformed to obtain the phonological parameters, and then the best conversion function is selected by the classification and regression tree CART. The development platform of the present invention is built on the personal computer, the milk magic 5 operating system. The environment development system tool is M/crojo/ir/sMa/C++ heart 0. The voice database used in the following embodiments of the present invention is a set of emotion parallel corpora. Among them, the same sentence text, the language The two voice files of the neutral and the target emotions are respectively recorded. In an embodiment of the present invention, the voice database includes 120 sentences of happy emotions, 115 sentences of sad emotions and 115 sentences of angry emotions.

Recorded by a female recorder, the sampling frequency is 22.05 kHz, and the resolution is 16 bits. X. Referring to the first figure, the system architecture diagram of the Chinese speech sound and rhyme conversion system 200935399 ίο according to an embodiment of the present invention, the Chinese speech phonological conversion system 10 The system is configured to receive a source speech and a corresponding source text, and then parse the source speech according to the linguistic features of the source text, and re-synthesize the source speech into synthesized speech with different emotional expressions. The Chinese phonetic phonetic conversion system 10 includes a speech analysis unit 20, a phoneme analysis unit 3A, a phoneme analysis unit 40, and a speech synthesis unit 50. The voice analysis unit 20 is configured to receive the source voice, extract the phoneme portion and the spectrum portion from the source voice, and provide the phoneme portion and the spectrum portion to the phoneme analysis unit 30 and the phoneme analysis unit 40, respectively. . The speech analysis unit 20 employs the STRAIGHT (Speech Transformation and Representation using Adaptive Interpolation of wei GHTed spectrum) algorithm to include the calculation of pitch and spectrum, and the algorithm for synthesizing the sound signal from the pitch and the spectrum. The speech analysis unit 2 ' mainly adapts the pitch through the spectrum analysis, and uses the Minimum Perturbation Operator in the analysis section of the phonology (pitch) to operate the base week from the time domain. Calculation. The phonological analysis unit 30 is configured to receive the phonological portion of the source speech and the source text corresponding to the source speech, and convert the phonological portion of the source speech according to the source text to generate a new phonological portion. The phonological analysis unit includes a hierarchical disassembly module (Hierarchical Dec〇mp〇Siti〇n) 3l, a function selection module 32, a phonological conversion module 33, and a phonological conversion library 34. . The hierarchical disassembly module 31 is based on the level of the sentence, and the part of the sentence is disassembled and quantified into different levels of pitch models and phoneme parameters, and each level of the sentence is passed by the received source text. Analyzed, where the content of the source text corresponds to the source of speech. The hierarchy disassembles the module 31 and provides a pitch model and a phoneme parameter to the function selection module 32. Among them, the quantization of the phonological parameters of 200935399 is to approximate the pitch of the pitch part by the curve of the pitch model, and calculate the coefficient of the pitch model from a continuous pitch trajectory value, and through the hierarchical decomposition. The phoneme portion (pitch trajectory) of the source speech is quantized into the syllable parameters of the sentence hierarchy, the word hierarchy and the sub-syllable hierarchy. The function selection module 32 selects a suitable phoneme conversion function based on the rhythm parameters generated by the hierarchical disassembly module 31 and the linguistic features of the received source text, J = _ CART model. The rhyme conversion module 33 selects the phonological conversion function of the sentence, the eulogy and the sub-syllable selected by the function selection module 32, and the phonological parameters generated by the hierarchical disassembly module 31 represent the phonological parameters. The conversion function gets the converted parameters. The phonetic conversion library 34 is used to store the phonetic conversion functions required for the phonetic conversion template 33. The phonological analysis unit 40 is configured to receive the spectrum of the source speech and convert the portion of the spectrum into a new portion of the spectrum. The phonological analysis unit 4 further includes a spectrum conversion module 41 and a spectral conversion library 42. The spectrum conversion module 41 extracts the spectrum parameter from the received spectrum portion, and substitutes the spectrum portion into a spectrum conversion function to obtain a converted spectrum parameter. The spectrum conversion library 42 is used to store the spectrum conversion function required by the spectrum conversion module 41. The spectrum conversion module 41 adopts a Gaussian Mixture Model (GMM)-based spectrum conversion function, and the spectrum conversion function is as follows: F (x) = ^ [y I χ] = X / (ί I x) [y I ,·]+Σ« ()_1 (x - £ [χ I /]) j 200935399 where χ denotes the input of the spectral part of the conversion function of the U Gaussian composition of the parameter f to 罝" represents the spectrum. The expected value of the spectral parameters of the second source speech, the covariance matrix of the bead u first sound, ί;:;: the matrix of the variance number, and the side of the face, the Gaussian composition of 10 with the real age segment ^ phonetic rhyme conversion system, practice conversion The system architecture diagram of the function, the Chinese voice set up the Ag clock to analyze the training speech and the target speech to generate the rhyme conversion function and the spectrum conversion function. ^Into the Chinese voice and rhyme conversion system 10 The phonological analysis unit 3 further regression-based clustering 35, a function $, a module (Supervised CART) 36 "and a problem module ((4) charm buckle: !37:. The training module 35 uses the regression grouping algorithm to train the training data 1 group and perform the phonetic conversion function. Training. The function classification module 36 classifies the training data with category information and the regression training of the regression tree CART to find the best problem for each node in the classification and regression tree CART, &quot;T will The knife is the most correct, so that each child node has the least disorder, that is, most of the data contained in the same category. The problem module 37 is used to store the problem set of the classification and regression tree CART, for example, In the current question of whether the word length of the word in the second syllable is two words?", each question can be divided into two groups that are in question and non-conforming. The phoneme analysis unit 40 of the Chinese phonetic phonetic conversion system 10 Then enter one

Step includes a spectrum training module (Expectation-Maximum Training, EM

Training] 43 The spectrum training module 43 uses the EM algorithm to estimate the parameters of the statistical model. 12 200935399 As described above, the Chinese Department of the present invention first classifies the sound eucalyptus CART required for the target speech training according to the received training and the vocabulary tree CART required for the target speech training. It is stored in the tone 1 conversion function according to the class and the return Chinese speech phonetic conversion system 1〇 receiving-conversion library 34. When the text is used, the Chinese phonetic phoneme is converted/original voice and the corresponding source multi-level pitch model and corresponding $G can be generated by the source speech. The volume is selected by the classification and regression tree CART1. The number, the rhythm parameters are generated by the 音 合成 合成 ❹ , , , , , , 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新 重新The present invention considers the phonological unit of the upper layer of the human body, implying a = layered manner to present, and the phonological unit of the lower layer is more than a single element, and the global tone of the sentence changes, and therefore, different classes The third graph corresponding to different subtle local tones will be the present invention - the real _ = = number. In this embodiment of the invention, the rhyme part of the hierarchy diagram. Three layers, respectively, the sentence hierarchy group 31 Disassemble the phonological part into the original phonological part, that is, the pitch, the skill and the sub-syllable level. It is also the word level and the sub-syllable level; the second is disassembled into the sentence level and processed separately. And the phonological parameters of each class, the hierarchical disassembly module, the use of the sentence layer can cut the H-touch phase and the sub-syllable steps caused by the riding sentence, and then analyze the sentence to be more local. Can also be made separately Different vocabulary changes. In addition, in each class, the solution module 31 can have a flexible 搔$ line approximation model, so that the phonological conversion function of the gradation parameter of the hierarchy is like the level of fineness of the class. 200935399 NDi Λ

J /=0 where '/(7) represents the time, the phoneme parameter, the number of all nodes in the z•th hierarchy' represents the pitch model used by the yth layer, and wg is the step function, indicating when (t&lt The ;ti,j) time value is 〇, indicating a value of 0 when (t&gt;j j+1). Therefore, under the limit of the step function, only the pitch value of one interval can be taken at a time, that is, the first node in the layer/layer. The phonological parameters corresponding to the pitch model of each class are obtained through analysis, and then the obtained phonogram parameters are compared with the original actual values to calculate the amount of error, that is, the value (Residual). Finally, the residual value obtained will enter the next layer for more detailed analysis. The residual difference is calculated as follows: 3〇+i(〇= :^Wg;〇) 〇•The phonological conversion function is Sentences, words, and sub-syllables are units of each level. Therefore, before the conversion, the original pitch curve must be entered and quantized. In an embodiment of the invention, the pitch model quantizes the original pitch curve by a Legendre P〇lynomial. The noisy Lei Jiande polynomial is a method of trajecting, approximating and quantifying by each of the orthogonal polynomial bases. According to the setting of different orders, the original input curve is set into the coefficients of each base. The higher the order, the more accurate the result is. . Using the polynomial to approximate the change of the original input curve, it is also possible to remove some of the error phenomenon of the pitch curve discontinuity caused by the two analysis of the sound. Reference is made to the fourth figure which is a schematic diagram of a Lei Jiande polynomial according to an embodiment of the present invention. Where ' when the order is (... when 4, the estimate of the Lei Jiande polynomial is as follows:

P Σα*φ/ /=〇η ·&gt; _ ΚΝ 0&lt;η&lt;Ν 200935399 ai N + N p 'η, Φ, I 0&lt;/&lt;3 e where the household name indicates the original input curve '# is the original input The total number of points of the curve, „the index of several points 屮 is the corresponding coefficient value, which is the zth-order 底 项 term', and its definition is as follows: soil η ~Ν =1 Φ, r ηΛ ί 12Ν ^ 1 2 ~ Μ Γ UJ {ν + 2) [ν) ~2_ η ~Ν ISON3 ❹ Φ, η ~Ν _(ΑΑ-ΐ)(Λ^ + 2)(^ + 3) 2S00N5 η ~Ν , 2 η ~Ν (Ν-ΐ)(Ν-2)(Ν+2)(Ν+3)(Ν+4) η ~Ν) \3 \2 6Nd Ν-1 6Ν

X 2 ζ}Ν+2(η'\ [Ν~\)(Ν~2) ΙΟΝ2 Ν 20Ν2 ❹ The sentence hierarchy is the uppermost layer in the hierarchical phonological model, so the sentence hierarchy considers the variation in the phonology belongs to the whole universe. Sexual effects. Due to the limitation of human pronunciation, the phonological changes of the sentence will change with the growth of the sentence, and the pitch of the pitch will be weak. Therefore, the tone intercepted in the sentence class must consider the sentence factor, that is, the pitch. The extent of weakness. Use two rounds of f to estimate the degree of pitch degradation. The pitch model established by the sentence hierarchy is as follows: ^〇(0 = ίϊ + bt ο where 'α represents the average pitch of the sentence, and the program presents the pitch 15 200935399 The path of weakness f ° Next, calculate The error value (residual difference value) between the estimated value and the actual value is analyzed in the lower layer. After the corresponding speech and the target are respectively determined for the source speech and the target, the phonological parameters of the sentence hierarchy are determined, and then the sentence level is changed. The training of the function. & In the f-class, there is a short pause between the word and the word, so there is a phenomenon in which the pitch is re-started between the 5th and the 1st. Therefore, the θ of the word hierarchy is considered. The rhyme factor 'is the pitch change trend inside the word unit. Compared with the sub-order a, in the clever class, consider the change of the sound item that is further refined than the sentence level, so the 'private third-order Lei Jiande polynomial ( Legendre Polynomial) Coefficient to build the pitch model of the class: ^η&lt;Ν立八中' For the training mode in the second picture, the source speech and the target language-: find the corresponding third-order Lei Jiande Polynomial parameter The rhythm parameter vector with dimension 3 can be obtained, and the function training module 35 can enter the word level: the training of the ΐ rhyme conversion function. The θ/人 θ section is the lowest phonological unit, the main phonological changes The tone change of „ is also the most direct response to the emotional part of the speaker's emotions. * A T声nai Language, each syllable and syllable. It is composed of initials (consonants) and tones. Therefore, if each tone syllable (she turns 1 in the unit), there are 1300 different sounds saved, which makes =23 to collect a large amount of corpus to train The corresponding conversion function. The parent syllable can be defined in the following format:

Syllable = (〇+(v,V(V,N). Rhyme where C stands for the initials (on the vowels of the Μ ( (/?_, and 16 200935399 females are subdivided into heads (F), body

Said, that is, 1 (HJJ), soft (ΜΜ) to describe. Take Fen Yun. The tone information, large, for the four tones of the sound (Low) and four (Faiiing), can be each high, (H) and low (L) 2 (LH), 3 (LL) 4 sounds (HL), and the use of sub-syllables H, L, not only can greatly reduce the need for corpus, the mother syllable cuts) L, M can describe each tone syllable can be more accurate estimate each Syllables. m T i is cut into the syllables corresponding to the tone of each syllable in the 1^ class towel, and each tone is simplified by combining L. Then the pitch model for each sub-syllable is built using a 4th-order Yunde polynomial, as follows:

Similarly, for the training mode in the second figure, the source speech and the target speech respectively obtain the corresponding rhythm parameters of the fourth-order Lei Jiande polynomial, and then the phonological parameter vector with the dimension of 4 can be obtained. The function training module 35 can perform the training of the phonetic conversion function of the corresponding phoneme parameters. Referring to the fifth figure, an example of a hierarchical phonological analysis is shown. As shown in the figure, the pitch parameters of the sentence hierarchy show that the pitch of the sentence is gradually rising, and the word ^ layer ^ phonological parameter indicates the pitch of the pitch in the word hierarchy, the sub syllable The phonological parameters of the class record the changes in the four tones of Chinese. Referring to the sixth figure, the comparison of the standard deviations of the hierarchical and non-hierarchical phonological parameters shows that the phonological parameters after the hierarchical analysis do have a small standard deviation, and the difference between the displayed phonological parameters is reduced. Therefore, the hierarchical analysis can gradually separate the global change from the upper layer to the lower layer. When the bottom layer is lower, the standard deviation will gradually become smaller. 17 200935399 The functional training module 35 is part of the training rhyme conversion function. 'The amount of data available due to the sentence level and the phrase level is small, and the related linguistic information is also less. Therefore, the phonological conversion in the two levels is The establishment of the function is based on the traditional Gansian hybrid model-based (GMM-based) transformation. In the Gaussian mixture model-based transformation method, the phoneme parameter vector after the source speech and the target speech are calibrated will exhibit a joint normal distribution. Let ^^^•^^^.•.^. , is the phoneme sequence of the phoneme part of the source speech '丫^^, y2,...,yn} is the phoneme sequence of the phoneme part of the target speech, and [χτ,yT]T is ❹, after calibrating The phoneme parameter vector pairs, and assume that the vector dimensions of X and y are both d'. According to the basic assumption, [xT, yT]T should conform to the joint normal distribution, so that the joint probability density function is: 〃 /(z) = /( x, y) = - then)]· V _ Εχχ Εχ γ This Σ τ: L^yx yy”0 © where ζ—[χΤ,ΥΤ] Τ 'And Ε[ζ]=[Ε[χ], E [y]] 7 is the average vector of ζ, and 2 is the matrix of the common variance of 2. Let the phonetic conversion function be /= factory^), and the goal is to find a phonetic conversion function so that there is a minimum mean square error between the phoneme parameter vector sequence of the target speech and the converted phoneme parameter vector sequence, that is, I hope that the sein conversion function can be found in the case of se=five [| the smallest case] and there is a minimum error. According to the criterion of Minhnum Mean Square Error (MMSE), it can be known that when the phoneme conversion function 夕=5 bk], there is a minimum error value, that is, the conversion function based on MMSE is F(x)=E. [y|x], and the conditional probability density function of the two is: 200935399 /(y|x)-£fezl=_!_ /(χ) (2π)Φά^(Χγγ-ΣγχΊ:^Ί: exp

V

~2Q Q =^^(^Μ + ΣυχΣ^(χ-£[χ])))Γ (Art yy-ΣγχΣχχΣχγ) (y-(五Ι^ + ΣγχΣ^^χ-Εΐχ]))) The phonological conversion function is: 〇纟^(Χ) = %ΙΦ%] + ΣνχΣ&amp;(Χ-Ε[χ]). The quasi-phonological parameters are not just a simple normal distribution. You should use a more refined type to interpret the rhythm parameters. Therefore, according to the definition of the Gaussian mixture model, the phonetic conversion function obtained under the Gaussian conditional normal distribution is then transmitted through the mixture s. ,! After the weights of the lower groups are summed up, a more detailed forecasting direction can be obtained. So the final phonetic conversion function of the Gaussian mixture model is: F(x) = ^[y | χ] = Σ/(^1 x)[y I /]+Σ« (ς« )_1 {χ~Ε[χ \ φj

/ {i I x)= Σ^(χ) ο ’, medium/(%) indicates the probability that x belongs to the next group. 1 In the sub-syllable class, because there is the largest amount of relative information, more linguistic information can be obtained. Therefore, in order to achieve a significant reduction in the amount of error = the use of regression homing to establish the phonetic conversion function. Since the regression line of the group 仏~母 represents a phonological conversion function, when converting, after the source value, since there is no corresponding target value, the corresponding conversion function must be selected by 1 mechanism. formula. Referring to the second figure, the training &amp; rhyme 19 200935399 counts the linguistic information to introduce the regression of the grouping according to the similarity of the g-study and the similarity of the linguistics as the grouping... Fruit:: Lee: Classification Regression tree to train the function to pick the model. Slogan: Get a description; from the source speech and the eye vector sequence Y={yi to the sequence x={xi,x2,.·.,w ' and the target phonology, then the linear regression of 2 mesh·;;= set The vector dimension with y is d, which satisfies the purpose of 'to find out the function relationship between Y and X, so that ® γ=/(χ)=Α)+Αχ. The parameter rotation is _ minimum flat method: Ε=Σ ^~(β〇+βιΧί)]2 y ββ η ^ = ~%^~{β〇+ β^)] = 〇θβι ~ ~ + Pixi)h = ο φ finally obtains linear regression parameters of d dimensions

Then, for each piece of data, regression homing is performed. When homing, the results of the combination of the similarity of the phonetic part and the similarity of the linguistic part must be considered. The phonetic similarity is calculated as follows: According to the basic assumptions of the linear regression model, the regression analysis must satisfy four points: 1. conditional normal distribution, 2. homogeneity, 3 linearity, and 4 independence. According to the assumption of the normal distribution of the first point condition, when the phoneme parameter vector of a source speech 20 200935399 is given, because of the error term factor, that is, 3, and the distribution of the assumed error term is a normal distribution, the target speech The distribution of the phoneme parameter vectors also exhibits a normal distribution. Therefore, when calculating the similarity of the phoneme parameter vector of the speech, it is estimated by using the conditional normal distribution to convert the error amount into the probability distribution: p{Ac〇ustic (y IX, = ^(y I Ρι(1)χ+P〇1 }, ^{1)) 第七 Referring to the seventh figure, an embodiment of Linguistic Feature ❹ Vector ajai, a2, ..., aL]. For each group, calculate the probability value of the linguistic feature parameters for each dimension, ie PM\C) =

Count Count (C) where CowwiO/, C) indicates the number of occurrences of the linguistic feature of the /th dimension in the Cth group, and C(10) plus (C) indicates the number of data points of the Cth group. Then, after multiplying the probability values of each dimension, the similarity of the language features is as follows: Finally, combining the similarities of the two feature vectors, the basis of the grouping is obtained:

Sim(C,S) = pAcoustic (y | x,C)° * PLinguistic (a | cfa = ^(y|PlX + P0,L)a ΙΟΓ)1 〇 where 5 is the data point and a is the weight, via The experimental analysis results are given. The present invention proposes a regression grouping algorithm, with the acoustic feature similarity PAc_iC(.,.) and the linguistic feature similarity PunguisticC.) to perform the grouping of the phonetic conversion function, and the execution steps of the grouping are: 1. Treat all the data as a group, find a corresponding regression parameter of a group, then within the interval of 3 times the variance, etc. 21 200935399 column; 2. Calculate each data point 3 C, ^ = parameter '4. Repeat step (4) 'After any two zeros, there is an arbitrary graph. The regression homing algorithm of the 式α归^^ phonological characteristic similarity is similar to the linguistic characteristics 〇ΐ==Γ: after the group, each regression line , which means - a factor == dijin =; two to _, the formula 1 line conversion, change the letter. The pre-pre (four) problem is the regular technique, using the tree-shaped knife structure to generate the corresponding rules. In the field of cc speech, it is often used to perform phonological predictions. Further, linguistics is also considered. In addition to the linguistics, the classification of the model established by the classification regression tree cart means that the structure of the model can be The importance of each attribute is roughly determined, and the rules it generates are also easy to understand. Therefore, in the stage of the establishment of the phonetic conversion function, the correct phoneme conversion function can be known for each source^ relative target, so ', in the mud, you can use the classification regression tree cart to establish each tone. t change the J-type corresponding rule, which means to establish the phonetic conversion function = capital == rhyme =; classification back to predict my handle ^ use the classification regression tree CART to establish the phonetic conversion function selection model, 】 linguistic Information is introduced, using language feature parameters and phonological parameters 22 200935399 for classification. In this lin (four) language predicate parameter, and the language feature parameters used are the same, so the weight of the regression J-likeness also affects the regression classification tree CART training 2 = reference ninth figure is the invention - implementation Example classification and regression tree c schematic. Its t, "T〇ne=3" indicates whether the current subtone is J three...—+ indicates the next two: whether the mouth is - sound; "Is Word len, 2" indicates the length of the current sub syllable, or Two words; "IsrightP.M. = '?," means that the current syllable = is a punctuation mark of "?,"; and rIs Initial = 6 i is the sixth part of the syllable of the syllable in which the syllable is located In the model established by the sub-classification regression tree, each leaf node, the phonetic conversion function. The split rule set by the root node - up to the leaf node is the corresponding rule for this phoneme conversion function.砀 Among them, the steps of the regression classification tree algorithm include: I will train the irirr sample set; 2. According to the splitting criterion, the training will be cracked to produce the first split point; 3. Using the test sample data Verify that the split is the best split' means that the interview sample towel finds a counterexample; 4. If the counterexample exists, move it into the training sample set and repeat otherwise to end the training and return to the regression classification tree. And the regression classification ^ the number of data is only 〗 〖 pen or less than - the number; 2. Every money has the same predicted value; 3. After the branch, the improvement of homogeneity is very limited and the split is determined. The algorithm has a non-splitting criterion. The purpose of this study is to maximize the purity of each node as much as possible, that is, the data points in each point belong to the same conversion function, and the split algorithm in this study is a modified version of ID3 Algorithm: C4 5 AlgQfith split principle. The splitting criterion is based on entropy (Entr〇py), which is calculated as the gain ratio 23 200935399 (Gain Ratio), calculated as follows:

Gain Ratio = (four) ropey Weighted Entropy

Split Gains where 'Entropy' is calculated as follows η

Entropy = Pi χ log(p.) /=1 ❹ number): The weight is worth given by (number of child nodes / parent node Δ)

SplitGains = p. x log(/?y) Μ 1 parent

Pj = data Number inNode Qing I data Number in Node Therefore, when the gain ratio is larger, it represents the purity of the post-split node, so each time the split (four) gains the Kawasaki split. Refer to the tenth ^ speech data library for the minimum mean square error and gain ratio of the statistics. ...χ After the post-in-service stage, the _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ The difference between the phonological parameters and the objective parameter is the criterion for objective evaluation. The calculation of the mean square error is as follows: MES=1 y=yi[ym^ymy Μ w=0 The number of phonological parameter vectors respectively represents the phonological parameter vector The smaller the mean square error is, the better the conversion effect is. As shown in the tenth-picture, before the weight α is less than 0.6, the mean square error 24 200935399 has a tendency to decrease with increasing weight, when the weight exceeds 〇· ί大二i angry mood performance is less obvious. The most predictive error of sad emotions has a smaller error. The correct rate is selected according to the tenth == formula, and the correct rate increases with the increase of weight α. The correct selection rate of the function is the worst. s Among them, the anger is the comparison ^ number pair conversion method (four), for the ϋ based conversion, the corresponding phonological conversion function of the group enters 0 and the (four) 'pre-sequence increases with the number of groups. Lower, pre (four) The method based on regression material can effectively reduce the phonological parametric formula, and will be 1414. The prediction error based on the regression homing group and the Gaussian mixture model. Ai ii conversion, given the same source parameter value. The proposed regression clustering algorithm can further reduce the conversion method:: ίί II, refer to the fifteenth figure. Under the three emotions, the square turtles mentioned in the present invention can perform better and have lower predictions. The error is mainly applicable to the combination of various human-machine single/two-way communication systems. The more and middle-level phonological model not only conforms to the phonological structure of human vocalization, but also reduces the variation of phonological parameters; The categorization algorithm, the prediction error on the low-parameter conversion, makes the phonological conversion more accurate. The computer speech synthesis system with more or more emotions can reduce the sound vocabulary and the electricity needs to be combined with various electronic education. Information exchange or transmission equipment, such as storybooks, airline reservation systems, information inquiry systems, etc., can create valuable information products. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the scope and spirit of the following claims, and the present invention is not limited by the description. An example of an embodiment.

26 200935399 [Brief Description] The first figure is a schematic diagram of a Chinese phonetic phoneme conversion system according to an embodiment of the present invention, and the second figure is a Chinese phonetic phoneme conversion system for training conversion function according to an embodiment of the present invention. The third diagram is a schematic diagram of a part of a phoneme portion according to an embodiment of the present invention; the fourth figure is a schematic diagram of a Lei Jiande polynomial according to an embodiment of the present invention; and the fifth figure is a hierarchical phoneme according to an embodiment of the present invention; A schematic diagram of analysis; φ sixth diagram is a comparison diagram of standard deviations of hierarchical and non-hierarchical phoneme parameters according to an embodiment of the present invention; and seventh figure is a schematic diagram of linguistic feature vectors according to an embodiment of the present invention; A schematic diagram of a regression grouping algorithm according to an embodiment of the present invention; a ninth diagram is a schematic diagram of a classification and regression tree according to an embodiment of the present invention; and a tenth diagram is a characteristic chart of a voice database according to an embodiment of the present invention; 11 is a schematic diagram of a weighting α affecting a prediction error of a phoneme parameter according to an embodiment of the present invention; 10th. FIG. 12 is a weight alpha shadow according to an embodiment of the present invention; Schematic diagram of the selection accuracy of the phoneme conversion function; FIG. 13 is a schematic diagram of the prediction error of the group number affecting the phoneme parameters according to an embodiment of the present invention; FIG. 14 is a regression-based grouping method according to an embodiment of the present invention A comparison diagram of a prediction error when converting with a Gaussian mixture model algorithm; and a fifteenth diagram is a comparison diagram of a regression grouping and a Gaussian mixture model algorithm according to an embodiment of the present invention. Main component symbol comparison description: 27 200935399 1 〇...Chinese speech phonological conversion system 20-speech analysis early 30... phonological analysis unit 31... hierarchical disassembly module 32... function selection module 33 - phonological conversion module 34 - phonology Conversion Library 35 - Function Training Core Group ❹ 36 - Function Classification Module 37 - Problem Module 40 - - Sound Analysis Early Element 41 - Spectrum Conversion Module 42... Spectrum Conversion Library 43 - Spectrum training module 50... speech synthesis unit

28

Claims (1)

200935399 X. Applying for patents: 1. A Chinese phonetic rhyme conversion system with a phonological analysis unit for receiving a source of speech and corresponding source text and establishing the phonological analysis unit including: 珉°°曰, -Turning module Defining the source text in a plurality of levels, each order sound &amp; has - (9) model ' and generating the pitch of the pitch model according to the source speech ^ β each (four) transfer = 1 ' according to the pitch model and corresponding The phonological parameters determine a phonological conversion module, and the number of execution diagnosing is converted into respective synthesizing phonological conversion functions to respectively convert the respective phonological ginseng-synthesis modules into two genre and type to generate the synthesized speech. D into the phonetic parameters to perform the corresponding pitch module 2. As claimed in the first item of the patent scope, the hierarchy includes sentence chip, \ text phonetic rhyme conversion system, wherein 3 · as claimed in the scope of the first item a And the sub-syllable level. The hierarchical disassembly module utilizes the Raven Voice Phonological Transformation System, where © the pitch model of the classes. &quot;&quot;Lendend Polynomial establishment 4. If the patent application scope is the first item, the layer disassembly module uses the speech and phonetic rhyme conversion system, among which the pitch model of the class is equal. The same pitch curve approximation model is established. 5. For example, in the scope of the patent application, in the hierarchical disassembly module, the sentence phonetic phonetic conversion system, in which the regression estimates the pitch attenuation. The level of the pitch model uses simple linearity. 6. As in the scope of the patent application, in the hierarchical disassembly module, the sentence phonetic phonetic transformation system, in which the parameters are used to the pitch of the pitch generated by the pitch model of the sentence hierarchy. The actual value of the high model is subtracted to produce a residual value (Residual) of 29 200935399, and the residual parameter is used to generate the rhythm parameter. The sound of the layer 7. The Chinese phonetic phoneme conversion in the second paragraph of the patent application scope is generated in the layer disassembly module, the pitch model of the word hierarchy is generated, and the second middle number is used to the pitch model of the word hierarchy. The actual value is subtracted, the rhyme is used to calculate the residual value, and the residual value is used to generate the sub-syllable level = one 8. For example, the Chinese speech phonological conversion system of the second item of the patent scope is 1 ❹ ❹ Section 9.::: The Chinese phonetic phoneme conversion system of the i-th patent range, wherein the μw-type selection module determines the phonetic conversion functions using a classification and regression tree (CART). 10. If you apply for a patent scope! The Chinese voice and rhyme conversion system of the item includes a function training module for generating the pitch conversion function. 11. If the Chinese speech sound and rhyme conversion system of the patent application scope is further selected to receive a target speech, the functional training module compares the phonetic parameters generated by the source speech and the target speech, and generates the phonetic conversion functions. . ❿12. The Chinese speech sound and rhyme conversion system of item 9 of the patent scope further includes a question module for storing the classification and regression tree problems. 13. The Chinese speech sound and rhyme conversion system of the first item of the fourth aspect of the invention, wherein the rhyme analysis unit further comprises a phonetic conversion function library for storing the phonetic conversion functions. 14. The Chinese phonetic phonetic rhyme conversion system of claim 2, wherein the phonetic conversion function of the sentence class and the word class is established by a Gaussian mixture model algorithm. 15. The Chinese phonetic phonetic rhyme conversion system of item 2 of the patent scope, in which the phonological conversion function of the human syllabary class is constructed by regression homing method. 2009 2009 399 立立 16. If the patent application scope is the third item of Chinese Yin Lili = - The speech analysis unit 'is used to split the i voice: 喟 喟 喟 喟 喟 喟 喟 喟 喟 喟 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 17 第 第 第 第 第 第 第 第 第 第 第 第 第 第The phonological analysis unit includes - spectrally dumping a plurality of spectral conversion functions, and - spectral transposing into a function - to convert the spectral portion: = source speech and generating a plurality of pitch models based on the source text The sound pattern of each pitch model generated according to the source speech is determined according to the pitch model and the corresponding phoneme parameter, and the rhythm conversion function of the rhyme conversion function is converted and converted. Converting the respective phonological parameters into their respective synthesized phonological parameters to execute the corresponding pitch model 'to generate the combined i9'm range and the 18th item, the text phonetic phonological conversion method, wherein two; == Genus A plurality of class' class system and such points 20.ίΓίί profit range Chinese phonology speech conversion method, Paragraph 19, of which 2 class system for the sentence strata, sectors and sub-syllable word class. The Chinese phonetic rhyme conversion method of the 18th item, wherein the 曰咼 model is established by using the Lei Jiande polynomial. 31 200935399 22. For the Chinese phonetic phonetic conversion method of claim 20, the pitch level, word class and sub-syllable level pitch models are respectively established using different pitch curve approximation models. 23. For the Chinese phonetic phonetic conversion method of claim 20, the pitch level model of the sentence class uses simple linear regression to estimate the pitch level. 〇24·If the Chinese phonetic phonetic conversion method of the second paragraph of the patent application scope, the pitch parameter generated by the pitch level model of the sentence is used to subtract the actual value of the pitch model of the sentence level layer to generate A residual value is used, and the residual value is used to generate a phoneme parameter of the word hierarchy. 25. ^Declaring the Chinese phonetic phonetic conversion method of the 20th patent range, wherein the phoneme parameters generated by the pitch model of the word hierarchy are used to subtract the actual value of the pitch model of the word hierarchy to generate a residual value. And using the residual value to generate a phonological parameter of the sub-syllable level. 26.2 Qing Chinese Patent Range No. 2 Chinese speech phonetic rhyme conversion method, in which = one 阶层 阶层 hierarchy, using H, L, M parts to combine to represent a syllable 7G 〇❹27 ΐΐί range of the 18th Chinese speech The rhyme conversion method, in which 28 commutative functions are categorized—classification and regression tree decisions. Step: The Chinese speech phonetic rhyme conversion method of the 18th patent range, the entry-to-house target speech, and the step-by-step comparison of the target speech and the source speech four-digit number, (10) the scalar tone-return function. The 18th Chinese speech phonetic rhyme conversion method, in which the mode is built. The rhyme conversion function is based on the Gaussian mixture model algorithm. 3〇.^Ϊ The patent range of the 18th Chinese phonetic phonetic conversion method, in which the human phonetic rhyme conversion function is established by regression grouping. 32 200935399 31. The Chinese phonetic phonetic conversion method of claim 18, further comprising disassembling the source speech into a phoneme portion and a spectrum portion, and generating a phoneme parameter of the pitch model according to the phoneme portion. . 32. The Chinese phonetic phoneme conversion method of claim 31, further comprising selecting a spectral conversion function to convert the portion of the spectrum into a synthesized spectral parameter. 33