WO2015025788A1 - 定量的ｆ０パターン生成装置及び方法、並びにｆ０パターン生成のためのモデル学習装置及び方法 - Google Patents

定量的ｆ０パターン生成装置及び方法、並びにｆ０パターン生成のためのモデル学習装置及び方法 Download PDF

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WO2015025788A1
WO2015025788A1 PCT/JP2014/071392 JP2014071392W WO2015025788A1 WO 2015025788 A1 WO2015025788 A1 WO 2015025788A1 JP 2014071392 W JP2014071392 W JP 2014071392W WO 2015025788 A1 WO2015025788 A1 WO 2015025788A1
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pattern
component
accent
phrase
generating
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French (fr)
Japanese (ja)
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晋富 倪
芳則 志賀
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National Institute of Information and Communications Technology
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National Institute of Information and Communications Technology
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Priority to EP14837587.6A priority Critical patent/EP3038103A4/en
Priority to CN201480045803.7A priority patent/CN105474307A/zh
Priority to KR1020167001355A priority patent/KR20160045673A/ko
Priority to US14/911,189 priority patent/US20160189705A1/en
Publication of WO2015025788A1 publication Critical patent/WO2015025788A1/ja
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    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/08—Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
    • G10L13/10—Prosody rules derived from text; Stress or intonation
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/08—Text analysis or generation of parameters for speech synthesis out of text, e.g. grapheme to phoneme translation, prosody generation or stress or intonation determination
    • G10L13/086—Detection of language
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L21/00—Speech or voice signal processing techniques to produce another audible or non-audible signal, e.g. visual or tactile, in order to modify its quality or its intelligibility
    • G10L21/02—Speech enhancement, e.g. noise reduction or echo cancellation
    • G10L21/0316—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude
    • G10L21/0364—Speech enhancement, e.g. noise reduction or echo cancellation by changing the amplitude for improving intelligibility
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/03—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters
    • G10L25/18—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 characterised by the type of extracted parameters the extracted parameters being spectral information of each sub-band
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L13/00—Speech synthesis; Text to speech systems
    • G10L13/02—Methods for producing synthetic speech; Speech synthesisers
    • G10L13/027—Concept to speech synthesisers; Generation of natural phrases from machine-based concepts

Definitions

  • the present invention relates to speech synthesis technology, and in particular, to a fundamental frequency pattern synthesis technology during speech synthesis.
  • the time change pattern of the fundamental frequency of speech helps to clarify sentence breaks, express accent positions, and distinguish words.
  • the F0 pattern also plays a major role in conveying non-verbal information such as emotions associated with utterances.
  • the F0 pattern has a great influence on the naturalness of speech. In particular, it is necessary to utter a sentence with an appropriate intonation in order to clarify the focused position during utterance and to clarify the structure of the sentence. If the F0 pattern is not appropriate, the intelligibility of the synthesized speech is impaired. Therefore, how to synthesize a desired F0 pattern in speech synthesis is a big problem.
  • Non-Patent Document 1 There is a method called the Fujisaki model disclosed in Non-Patent Document 1 described later as a method for synthesizing the F0 pattern.
  • the Fujisaki model is an F0 pattern generation process model that quantitatively describes an F0 pattern with a small number of parameters.
  • the F0 pattern generation process model 30 is adapted to F0 pattern, the phrase component, and the accent component, expressed as the sum of the basis components F b.
  • Phrase component refers to a component of an utterance that has a peak that rises immediately after the start of one phrase, and that gradually changes to the end of the phrase.
  • An accent component refers to a component represented by local unevenness corresponding to a word.
  • the phrase component is represented by a response of the phrase control mechanism 42 to the phrase command 40 on the impulse generated at the beginning of the phrase.
  • the accent component is similarly represented by a response of the accent control mechanism 46 to the step-like accent command 44.
  • the logarithm expression log e F0 (t) of the F0 pattern 50 is obtained by adding the phrase component, the accent component, and the logarithm log e Fb of the base component Fb by the adder 48.
  • an HMM Hidden Markov Model
  • a voice corpus As a typical method for constructing a model from a large amount of collected voice data, an HMM (Hidden Markov Model) is used based on an F0 pattern observed by a voice corpus as described in Non-Patent Document 2 described later.
  • HMM Hidden Markov Model
  • a conventional speech synthesis system 70 includes a model learning unit 80 for learning an HMM model for F0 pattern synthesis from a speech corpus, and a synthesized speech signal 118 corresponding to input text. And a speech synthesizer 82 for synthesizing according to the F0 pattern obtained using the HMM obtained by learning.
  • the model learning unit 80 extracts a speech corpus storage device 90 that stores a speech corpus with a phoneme context label, and F0 that extracts a speech signal of each utterance in the speech corpus stored in the speech corpus storage device 90.
  • the spectral parameter extraction unit 94 that similarly extracts a mel cepstrum parameter as a spectral parameter from each utterance, and the F0 pattern extracted by the F0 extraction unit 92 and the speech corpus storage device 90.
  • the context label is a control symbol for speech synthesis, and is a label to which various language information (context) such as the phonemic environment is assigned to the phoneme.
  • the speech synthesizer 82 receives the HMM storage device 110 that stores the parameters of the HMM trained by the HMM learning unit 96 and, when given the text to be speech synthesized, performs text analysis on the text, From the text analysis unit 112 and the text analysis unit 112 for specifying the word and the phoneme being uttered, determining the accent, determining the insertion position of the pose, determining the type of sentence, etc., and outputting a label string representing the utterance
  • the HMM stored in the HMM storage device 110 is collated with this label string, and a combination having the highest probability is generated and output as a combination of the F0 pattern and the mel cepstrum string when the original text is uttered.
  • the parameter generation unit 114 that performs the parameter generation and the F0 pattern given by the parameter generation unit 114
  • the sound represented by the mel-cepstral parameters given from the generation unit 114 synthesizes, and a speech synthesizer 116 for outputting a synthesized speech signal 118.
  • this speech synthesis system 70 it is possible to obtain an effect that various F0 patterns can be output in a wide range of contexts based on a large amount of speech data.
  • the present invention provides an apparatus and method for synthesizing an F0 pattern in which the correspondence between the linguistic information and the F0 pattern becomes clear while maintaining accuracy when the F0 pattern is generated by a statistical model. With the goal.
  • Another object of the present invention is to provide a device that can clearly set the focus of a sentence and can clearly set the correspondence between linguistic information and the F0 pattern while maintaining accuracy when generating the F0 pattern by a statistical model. It aims to provide a method.
  • the quantitative F0 pattern generation device generates an accent component of an F0 pattern using a given number of target points for an utterance accent phrase obtained by text analysis.
  • Each accent phrase is described by 3 or 4 target points. Two of the four points are a low target indicating a low frequency portion of the F0 pattern of the accent phrase, and the remaining one or two points are a high target indicating a high frequency portion of the F0 pattern. If there are two high targets, their strengths may be the same.
  • the means for generating the F0 pattern generates a continuous F0 pattern.
  • the quantitative F0 pattern generation method generates an F0 pattern accent component using a given number of target points for an utterance accent phrase obtained by text analysis. And generating a phrase component of the F0 pattern using a limited number of target points by dividing the utterances into groups containing one or more accent phrases according to linguistic information including the structure of the utterances; Generating an F0 pattern based on the accent component and the phrase component.
  • the quantitative F0 pattern generation device stores parameters of a generation model for generating a target parameter for the phrase component of the F0 pattern and a generation model for generating a target parameter for the accent component of the F0 pattern.
  • a model storage means a text analysis means for receiving a text input for speech synthesis, a text analysis means for outputting a control symbol string for speech synthesis, and a phrase component generating a control symbol string output by the text analysis means.
  • the model storage means may further store the parameters of the generation model for estimating the micro-prosody component of the F0 pattern.
  • the F0 pattern generation apparatus further includes a micro-prosody component output unit that outputs a micro-prosody component of the F0 pattern by collating the control symbol string output from the text analysis unit with a generation model for generating the micro-prosody component.
  • the F0 pattern generation means includes means for generating an F0 pattern by synthesizing the phrase component generated by the phrase component generation means, the accent component generated by the accent component generation means, and the micro-prosody component.
  • the quantitative F0 pattern generation method stores parameters of a generation model for generating a target parameter for the phrase component of the F0 pattern and a generation model for generating the target parameter for the accent component of the F0 pattern.
  • a method for generating a quantitative F0 pattern using the model storage means comprising: a text analysis step for receiving text input for speech synthesis, analyzing the text, and outputting a control symbol string for speech synthesis; Phrase component generation means for generating the phrase component of the F0 pattern by collating the output control symbol string with the generation model for generating the phrase component stored in the storage means, and the control symbol output in the text analysis step
  • the column is a generation mode for generating accent components stored in the storage means.
  • a model learning apparatus for generating an F0 pattern includes an F0 pattern extracting unit that extracts an F0 pattern from an audio data signal, and an F0 pattern that fits the extracted F0 pattern as a phrase component and an accent.
  • a parameter estimation unit for estimating a target parameter representing a phrase component and a target parameter representing an accent component, and a target parameter for the phrase component and a target parameter for the accent component estimated by the parameter estimation unit, Model learning means for learning the F0 generation model using the continuous F0 pattern represented by the following as learning data.
  • the F0 generation model may include a generation model for generating a phrase component and a generation model for generating an accent component.
  • the model learning means uses the phrase component time change pattern represented by the phrase component target parameter estimated by the parameter estimation means and the accent component time change pattern represented by the accent component target parameter as learning data.
  • First model learning means for learning a generation model for generating a phrase component and a generation model for generating an accent component is included.
  • the model learning apparatus further separates the micro-prosody component from the F0 pattern extracted by the F0 pattern extraction unit, and learns the generation model for generating the micro-prosody component using the micro-prosody component as learning data.
  • Second model learning means to perform may be included.
  • a model learning method for F0 pattern generation includes an F0 pattern extraction step for extracting an F0 pattern from an audio data signal, and an F0 pattern that fits the F0 pattern extracted in the F0 pattern extraction step. Is expressed by superimposing the phrase component and the accent component, a parameter estimation step for estimating a target parameter representing the phrase component and a target parameter representing the accent component, a target parameter of the phrase component estimated in the parameter estimation step, and A model learning step of learning an F0 generation model using a continuous F0 pattern represented by the target parameter of the accent component as learning data.
  • the F0 generation model may include a generation model for generating a phrase component and a generation model for generating an accent component.
  • the model learning step uses, as learning data, a time change pattern of the phrase component represented by the target parameter of the phrase component estimated in the parameter estimation step and a time change pattern of the accent component represented by the target parameter of the accent component. Learning a generation model for generating a phrase component and a generation model for generating an accent component.
  • FIG. 11 is a block diagram showing a hardware configuration of a computer after the computer system shown in FIG.
  • an HMM is used as the F0 pattern generation model, but the model is not limited to the HMM.
  • CART Classification and Regression Tree
  • JHFriedman JHFriedman
  • RA Olshen CJStone
  • CJStone Simulated annealing
  • CD Gellett, Jr. CD Gellett, Jr.
  • MP Vecchi "Optimization by simulated annealing," IBM Thomas J. Watson Research Center, Yorktown Heights, NY, 1982.
  • the basic concept of the present invention is as follows. First, an F0 pattern is extracted from the speech corpus, and an observed F0 pattern 130 is created. This observed F0 pattern is usually discontinuous. The discontinuous F0 pattern is made continuous and smoothed to generate a continuous F0 pattern 132. Up to this point, it can be realized using the prior art.
  • the continuous F0 pattern 132 is fitted by combining the phrase component and the accent component, and the F0 pattern 133 after the fit is estimated.
  • HMM learning is performed by the same method as in Non-Patent Document 2, and the HMM parameters after learning are stored in the HMM storage device 139.
  • the estimation of the F0 pattern 145 can be performed in the same manner as the method of Non-Patent Document 2.
  • the feature vector here includes 40 mel cepstrum parameters including 0th order and logarithm of F0, and their delta and delta delta as elements.
  • the obtained continuous F0 pattern 132 is decomposed into an accent component 134, a phrase component 136, and a micro-prosody component (hereinafter also referred to as “micro component”) 138.
  • micro component a micro-prosody component
  • the HMMs 140, 142, and 144 are separately learned for these.
  • the learning of the HMMs 140, 142, and 144 uses a feature vector that is integrated into a multi-stream format for these three HMMs.
  • the structure of the feature vector used is the same as that in the first embodiment.
  • the accent component HMM 140, the phrase component HMM 142, and the micro component HMM 144 are used to individually generate the F0 pattern accent component 146, phrase component 148, and micro component 150. These are added by the adder 152 to generate a final F0 pattern 154.
  • the micro component can be considered as a component obtained by removing the accent component and the phrase component from the F0 pattern. Therefore, how to obtain an accent component and a phrase component becomes a problem.
  • target points In both the accent component and the phrase component, the description at the target point is a method of describing one accent or phrase with three or four points. Two of the four points represent the low target and the remaining one or two points represent the high target. These are called target points. When there are two high targets, the strength is the same for both.
  • a continuous F0 pattern 174 is generated from the observed F0 pattern 170. Further, the continuous F0 pattern 174 is divided into phrase components 220 and 222 and accent components 200, 202, 204, 206, and 208, and each is described as a target point.
  • a target point for accent is called an accent target
  • a target point for a phrase is called a phrase target.
  • the continuous F0 pattern 174 is represented by an accent component on the phrase component 172.
  • the reason why the accent component and the phrase component are described at the target point in this way is to appropriately process the nonlinear interaction between the accent component and the phrase component by defining them in relation to each other. It is relatively easy to find the target point from the F0 pattern.
  • the transition of F0 between target points can be represented by interpolation by the Poisson process (Non-patent Document 3).
  • the F0 pattern is modeled here by a two-level mechanism.
  • an accent component and a phrase component are generated by a mechanism using a Poisson process.
  • these are synthesized by a mechanism using resonance to generate an F0 pattern.
  • the micro component is obtained by removing the accent component and the phrase component from the continuous F0 pattern obtained first.
  • Non-Patent Document 4 mapping using resonance (Non-Patent Document 4) is applied, and potential interference between an accent component and a phrase component is handled as a kind of topology conversion.
  • ⁇ f ⁇ 1 ( ⁇ ) is the inverse mapping of the above mapping.
  • Equation 4 represents the decomposition of lnf 0 on the time axis. More specifically, ⁇ f0r represents a phrase component (handled as a reference value), and ⁇ f0
  • f0r represents an accent component.
  • a non-linear interference between the accent component and the phrase component can be processed using a mechanism using resonance and integrated to obtain an F0 pattern.
  • a model expressing the F0 pattern as a function of time t can be expressed as a superposition of the accent component Ca (t) on the phrase component Cp (t) by resonance in logarithmic expression.
  • the model parameters representing the F0 pattern of the utterance are as follows.
  • the phrase target ⁇ pi is defined by F0 in the range of [f 0b , f 0t ] in logarithmic expression.
  • the accent target ⁇ ai is expressed in a range of (0, 1.5) with 0.5 as a zero point. If the accent target ⁇ ai ⁇ 0.5, the accent component bites into the phrase component (a part of the phrase component is removed), and lowers the end of the F0 pattern so that it can be observed by natural speech. That is, the accent component is superimposed on the phrase component, but at this time, it is allowed that a part of the phrase component is removed by the accent component.
  • FIG. 5 is a program of a control structure shown in a flowchart format, and processing for extracting the F0 pattern from the observed F0 pattern 130 shown in FIG. 3, and smoothing and continuation of the extracted F0 pattern 132 ,
  • the estimation of the target parameter for representing the continuous F0 pattern 132 by the sum of the phrase component and the accent component represented by the target point, and the F0 pattern 133 fitted to the continuous F0 pattern 132 by the estimated target parameter Has a function of performing generation processing.
  • this program smoothes the observed discontinuous F0 patterns, and continuously outputs the continuous F0 patterns, and outputs the continuous F0 patterns output in step 340 to N groups. And step 342 of dividing into two.
  • Each of the divided groups corresponds to an exhalation paragraph.
  • a continuous F0 pattern is smoothed using a long window width, a designated number of locations where the F0 pattern is a valley are detected, and the F0 pattern is divided there.
  • This program further minimizes the error between step 344 for substituting 0 for the repetition control variable k, step 346 for initializing the phrase component P, and the phrase component P and accent component A and the continuous F0 pattern.
  • Step 348 for estimating the target parameter for the accent component A and the target parameter for the phrase component P;
  • Step 354 for adding 1 to the repetition control variable k after step 348; and the repetition for which the value of the variable k is predetermined. It is determined whether or not it is smaller than the number n.
  • step 356 returns the control flow to step 346, and when the determination at step 356 is NO, the accent obtained by repeating step 346 to step 356 Optimize the target parameters of the accent, and the optimized accent target and phrase And a step 358 of outputting the Getto.
  • the error between the F0 pattern represented by these and the original continuous F0 pattern corresponds to the micro-prosody component.
  • Step 348 includes a step 350 for estimating an accent target parameter and a step 352 for estimating the target parameter of the phrase component P using the accent target parameter estimated in step 350.
  • Pre-processing F0 pattern is converted into ⁇ f0
  • f0r with f 0r f 0b and smoothed together with two window sizes (short-term: 10 points, long-term: 80 points) (step 340). Considering the characteristics of Japanese accent, rising-(flat)-falling, remove the influence of micro-prosody (change F0 using phoneme segments). The smoothed F0 pattern is returned to F0 using equation (5) for parameter extraction.
  • Step 342 Parameter extraction A segment between pauses longer than 0.3 seconds is regarded as an exhalation paragraph, and the exhalation paragraph is further divided into N paragraphs using an F0 pattern smoothed by a long-term window (step 342). The following processing is applied to each group. At this time, a criterion of minimizing the absolute value of the F0 error is used. Thereafter, the repeated control variable k is set to 0 in order to repeatedly execute Step 348 (Step 344).
  • A As an initial value, a phrase component P of three target points having two low target points and one high target point is prepared (step 346). This phrase component P has the same shape as the left half of the graph of the phrase component P at the bottom of FIG. 4, for example.
  • the timing of this high target point is matched with the start of the second mora, and the first low target point is shifted earlier by 0.3 seconds.
  • the timing of the second low target point is matched with the end of the exhalation paragraph.
  • the initial value of the phrase target intensity ⁇ pi is determined using an F0 pattern smoothed using a long-term window.
  • the accent component A is calculated using the smoothed F0 pattern and the current phrase component P according to equation (4). Further, an accent target point is estimated from the current accent component A.
  • C A range of [0.4, 0.6] for all low target points such that ⁇ ai is a range of [0.9, 1.1] for all high target points.
  • the accent component A is recalculated using the adjusted target point (step 350).
  • D The current accent component A is calculated and the phrase target is re-estimated (step 352).
  • E In order to repeat the return to (b) until a predetermined number of times is reached, 1 is added to the variable k (step 354).
  • (F) Insert a high phrase target point if the amount of error reduction between the generated F0 pattern and the smoothed F0 pattern is greater than a certain threshold by inserting a high phrase target point.
  • (b) In order to determine whether or not to return to the above (b), 1 is added to the variable k in step 354. If the value of variable k has not reached n, control returns to step 346. By this processing, for example, a phrase component P like the right half of the lower part of FIG. 4 is obtained. If the value of variable k has reached n, accent parameters are optimized in step 358.
  • step 358 On the premise of the estimated phrase component P, the accent target point is optimized so as to minimize the error between the generated F0 pattern and the observed F0 pattern. As a result, the target points of the phrase component P and the accent component A that can generate the F0 pattern that fits the smoothed F0 pattern are obtained.
  • the micro-prosody component M is obtained from the portion corresponding to the difference between the smoothed F0 pattern and the F0 pattern generated from the phrase component P and the accent component A.
  • FIG. 6 shows an example in which the phrase component P and the accent component A are synthesized according to the result of analyzing the text, and the F0 pattern is fitted to the observed F0 pattern.
  • FIG. 6 shows two cases superimposed.
  • the target F0 pattern 240 (observed F0 pattern) is represented by a symbol “+” column.
  • the first case shown in FIG. 6 is to obtain a fitted F0 pattern 246 by synthesizing an accent component 250 also indicated by a broken line with a phrase component 242 indicated by a broken line.
  • an F0 pattern 246 is obtained by synthesizing an accent component 252 also indicated by a thin line with a phrase component 244 indicated by a thin line.
  • the accent component 250 and the accent component 252 almost coincide with each other, but the positions of the high target point of the first accent element and the low target point on the back side are lower than those of the accent component 252. It has become.
  • a phrase component 242 composed of two phrases is adopted as a phrase component and synthesized with an accent component 252 obtained by a Japanese accent pattern. If the result of text analysis is that there are three exhalation paragraphs, the phrase component 244 and the accent component 250 are synthesized.
  • both the phrase component 242 and the phrase component 244 have a phrase boundary between the third accent element and the fourth accent element.
  • the phrase component 244 is employed.
  • a high target point and a low target point on the back side of the accent element located immediately before this position are represented as in the accent component 250. Pull down.
  • the F0 pattern can be accurately fitted to the result of text analysis even when three phrases exist as a result of text analysis.
  • the linguistic information that forms the basis of the utterance is represented by the utterance structure and the accent type, and the correspondence between the linguistic information and the F0 pattern is clear. by.
  • F0 pattern synthesis section 359 is obtained by smoothing and continually observing observed F0 pattern 130 from each of a large number of speech signals included in the speech corpus.
  • a parameter estimation unit 366 that estimates a target point that defines the phrase component P and a target parameter that defines the accent component A, and a parameter estimation unit 366 based on the given accent boundary and the target parameter that defines the accent component A, for the continuous F0 pattern 132
  • the F0 pattern fitting unit 368 that generates the F0 pattern after fitting that fits the continuous F0 pattern by synthesizing the phrase component P and the accent component A estimated by the above, and using the F0 pattern after fitting as in the conventional case HMM learning unit 369 that performs HMM learning, and after learning
  • a HMM storage device 370 for storing the HMM parameters.
  • the process of synthesizing the F0 pattern 372 using the HMM stored in the HMM storage device 370 can be realized by an apparatus similar to the speech synthesis unit
  • the system according to the first embodiment operates as follows. For each of the observed F0 patterns 130, a continuous F0 pattern 132 is obtained by smoothing and continuation.
  • the parameter estimation unit 366 decomposes the continuous F0 pattern 132 into the phrase component P and the accent component A, and estimates each target parameter by the method described above.
  • the F0 pattern fitting unit 368 combines the phrase component P and the accent component A expressed by the estimated target parameter, and obtains the F0 pattern after fitting that fits the observed F0 pattern. This system performs such an operation for each observation F0 pattern 130.
  • the HMM learning unit 369 performs HMM learning by using a number of F0 patterns after fitting obtained in this way, using a method similar to the conventional one.
  • the HMM storage device 370 stores the HMM parameters after learning. After the learning of the HMM is completed, when a text is given, the text is analyzed and the F0 pattern 372 is synthesized by using the HMM stored in the HMM storage device 370 according to the result. By using this F0 pattern 372 and a speech parameter string such as a mel cepstrum selected according to the phoneme of the text, a speech signal can be obtained in the same manner as in the prior art.
  • HMM learning was performed according to the first embodiment, and a subjective evaluation (preference evaluation) test was performed on speech synthesized using the F0 pattern synthesized using the learned HMM.
  • the experiment of this evaluation test was conducted using 503 utterances included in the voice corpus ATR503set created and published by the applicant. Of the 503 utterances, 490 utterances were used for HMM learning, and the rest were used for testing.
  • the speech signal was sampled at a sampling rate of 16 kHz, and the spectral envelope was extracted by STRIGHT analysis with a 5 ms frame shift.
  • the feature vector consists of 40 mel cepstrum parameters including 0th order, log F0, and their delta and delta delta. A 5-state left-to-right unidirectional HMM model topology was used.
  • F0 pattern obtained from speech waveform (original) (2) F0 pattern (Proposed) generated by the first embodiment (3)
  • the voiced portion is the original, and the unvoiced portion is the F0 pattern generated by the method of the first embodiment (Prop. + MP (Micro-production)).
  • Voiced part is original, unvoiced part is F0 pattern using spline interpolation (Spl + MP) Of the above four patterns, (2) to (4) are continuous F0 patterns. (2) does not include micro-prosody or F0 extraction error, but it should be noted that (3) and (4) include both.
  • a continuous F0 pattern was synthesized using a continuous F0 pattern HMM, and voiced / unvoiced were judged using an MSD-HMM.
  • the phrase component P and the accent component A are represented by target points, and the F0 pattern is fitted by combining them.
  • the idea of using target points is not limited to this first embodiment.
  • the F0 pattern observed by the method described above is separated into a phrase component P, an accent component A, and a micro-prosody component M, and HMM learning is performed for each of these time change patterns.
  • the time change patterns of the phrase component P, the accent component A, and the micro-prosody component M are obtained using the learned HMM, and the F0 pattern is estimated by further combining them.
  • speech synthesis system 270 uses model learning unit 280 that performs HMM learning for speech synthesis, and HMM that is trained by model learning unit 280, and the text is A speech synthesis unit 282 that synthesizes the speech when it is input and outputs the synthesized speech signal 284.
  • the model learning unit 280 includes a speech corpus storage device 90, an F0 extraction unit 92, and a spectrum parameter extraction unit 94, similar to the model learning unit 80 of the conventional speech synthesis system 70 shown in FIG. However, in place of the HMM learning unit 96 of the model learning unit 80, the model learning unit 280 smoothes the discontinuous F0 pattern 93 output from the F0 extraction unit 92, and performs continuous F0 smoothing that outputs a continuous F0 pattern 291.
  • Unit 290 and the continuous F0 pattern output by the F0 smoothing unit 290 are separated into a phrase component P, an accent component A, and a micro-prosody component M, and a time-varying pattern for each component is generated.
  • the model learning unit 280 includes a multi-stream HMM learning data vector 293 (40 mel cepstrum parameters including the 0th order) including the mel cepstrum parameter 95 output from the spectrum parameter extraction unit 94 and the output of the F0 separation unit 292. And the three component time variation patterns of F0, and their delta and delta delta), based on the phoneme context label corresponding to the learning data vector 293 read from the speech corpus storage device 90, And an HMM learning unit 294 that performs simple learning.
  • the speech synthesis unit 282 includes an HMM storage device 310 that stores the HMM learned by the HMM learning unit 294, the same text analysis unit 112 as that shown in FIG. 2, and a context label string given from the text analysis unit 112
  • HMM storage device 310 that stores the HMM learned by the HMM learning unit 294, the same text analysis unit 112 as that shown in FIG. 2, and a context label string given from the text analysis unit 112
  • a parameter generation unit 312 that estimates and outputs a pattern and a mel cepstrum parameter, and an F0 pattern by synthesizing a temporal change pattern of the phrase component P, the accent component A, and the micro-prosody component M output by the parameter generation unit 312 F0 pattern synthesis unit 314 that generates and outputs Including a mel cepstral parameters parameter generating unit 312 outputs, to synthesize a speech from the F0 pattern F0 pattern synthesizing section 314 outputs, to the same speech synthesizer 116 as shown in FIG.
  • the control structure of the computer program for realizing the F0 smoothing unit 290, the F0 separating unit 292, and the HMM learning unit 294 shown in FIG. 9 is the same as that shown in FIG.
  • the speech synthesis system 270 operates as follows.
  • the voice corpus storage device 90 stores a large amount of speech signals.
  • the speech signal is stored in units of frames, and a phoneme context label is attached to each phoneme.
  • the F0 extraction unit 92 outputs a discontinuous F0 pattern 93 from the utterance signal of each utterance.
  • the F0 smoothing unit 290 smoothes the discontinuous F0 pattern 93 and outputs a continuous F0 pattern 291.
  • the F0 separation unit 292 receives the continuous F0 pattern 291 and the discontinuous F0 pattern 93 output from the F0 extraction unit 92, and in accordance with the above-described method, the temporal change pattern of the phrase component P and the accent component A for each frame.
  • the HMM learning unit 294 for each frame of the speech signal of each utterance, the label read from the speech corpus storage device 90, the learning data vector 293 given from the F0 separation unit 292, and the mel cepstrum parameter from the spectrum parameter extraction unit 94.
  • the feature vector having the above-described configuration is used as learning data and the context label of the estimation target frame is given, the temporal change pattern of the phrase component P, the accent component A, and the micro-prosody component M of the frame.
  • Statistical HMM learning is performed so as to output the probability of the value of the cepstrum parameter.
  • the parameters of the HMM are stored in the HMM storage device 310.
  • the speech synthesizer 282 When a text to be synthesized is given, the speech synthesizer 282 operates as follows.
  • the text analysis unit 112 analyzes the given text, generates a context label string indicating the speech to be synthesized, and provides the parameter generation unit 312 with the context label string.
  • the parameter generation unit 312 refers to the HMM storage device 310 for each label included in the label string, so that the parameter string (phrase component P) having the highest probability of being a voice for generating such a label string for the label string.
  • Accent component A and micro-prosody component M, and mel cepstrum parameters) are sent to the F0 pattern synthesizer 314 as mel cepstrum parameters.
  • the speech synthesizer 116 are given to the speech synthesizer 116, respectively.
  • the F0 pattern synthesis unit 314 synthesizes the time change patterns of the phrase component P, the accent component A, and the micro-prosody component M, and gives them to the speech synthesizer 116 as an F0 pattern.
  • the phrase component P, the accent component A, and the micro-prosody component M are all expressed logarithmically during HMM learning. Therefore, in the synthesis of the F0 pattern synthesis unit 314, these may be added to each other after being converted from logarithmic expressions to normal frequency components. At this time, since the zero point of each component is moved during learning, an operation to restore the zero point is also necessary.
  • the voice synthesizer 116 synthesizes a voice signal according to the F0 pattern output from the F0 pattern synthesizer 314, and further performs signal processing corresponding to modulating the voice signal according to the mel cepstrum parameter provided from the parameter generator 312. And a synthesized voice signal 284 is output.
  • the F0 pattern is decomposed into a phrase component P, an accent component A, and a micro-prosody component M, and separate HMM learning is performed using them.
  • the phrase component P, the accent component A, and the micro-prosody component M are separately generated using these HMMs based on the result of text analysis.
  • an F0 pattern can be generated. If the F0 pattern obtained in this way is used, natural speech can be obtained as in the first embodiment.
  • the correspondence between the accent component A and the F0 pattern is clear, it is possible to easily focus on the word by increasing the range of the accent component A for a specific word. This can be seen from, for example, the operation of lowering the frequency for the component immediately before the vertical line 254 in the accent component 250 of FIG. 6 and the operation of lowering the frequency of the last F0 pattern in the accent components 250 and 252 of FIG.
  • Both the F0 pattern synthesis unit according to the first embodiment and the second embodiment can be realized by computer hardware and a computer program executed on the computer hardware.
  • FIG. 10 shows the external appearance of this computer system 530
  • FIG. 11 shows the internal configuration of the computer system 530.
  • the computer system 530 includes a computer 540 having a memory port 552 and a DVD (Digital Versatile Disc) drive 550, a keyboard 546, a mouse 548, and a monitor 542.
  • DVD Digital Versatile Disc
  • the computer 540 includes a CPU (Central Processing Unit) 556, a bus 566 connected to the CPU 556, the memory port 552, and the DVD drive 550, and a boot program. And the like, a read only memory (ROM) 558 for storing etc., a random access memory (RAM) 560 connected to the bus 566 for storing program instructions, system programs, work data, etc.
  • Computer system 530 further includes a network interface (I / F) 544 that provides a connection to a network 568 that allows communication with other terminals.
  • I / F network interface
  • a computer program for causing the computer system 530 to function as each functional unit of the F0 pattern generation / synthesis unit according to the above-described embodiment is stored in the DVD 562 or the removable memory 564 mounted in the DVD drive 550 or the memory port 552, and Transferred to the hard disk 554.
  • the program may be transmitted to the computer 540 through the network 568 and stored in the hard disk 554.
  • the program is loaded into the RAM 560 when executed.
  • the program may be loaded directly into the RAM 560 from the DVD 562, from the removable memory 564, or via the network 568.
  • This program includes an instruction sequence including a plurality of instructions for causing the computer 540 to function as each functional unit of the F0 pattern synthesis unit according to the above embodiment.
  • Some of the basic functions necessary to cause computer 540 to perform this operation are provided by operating systems or third party programs that run on computer 540 or various programming toolkits or program libraries installed on computer 540. . Therefore, this program itself does not necessarily include all functions necessary for realizing the system and method of this embodiment.
  • the program is a system as described above by dynamically calling the appropriate program in the appropriate function or programming toolkit or program library at run time in a controlled manner to obtain the desired result. It is only necessary to include an instruction for realizing the function. Of course, all necessary functions may be provided only by the program.
  • the present invention can be used to provide services using speech synthesis and to manufacture devices using speech synthesis.

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PCT/JP2014/071392 2013-08-23 2014-08-13 定量的ｆ０パターン生成装置及び方法、並びにｆ０パターン生成のためのモデル学習装置及び方法 Ceased WO2015025788A1 (ja)

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EP14837587.6A EP3038103A4 (en) 2013-08-23 2014-08-13 Quantitative f0 pattern generation device and method, and model learning device and method for generating f0 pattern
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