EP1267327A2 - Optimierung von Modellparametern zur Sprachkodierung - Google Patents

Optimierung von Modellparametern zur Sprachkodierung Download PDF

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Publication number
EP1267327A2
EP1267327A2 EP02005056A EP02005056A EP1267327A2 EP 1267327 A2 EP1267327 A2 EP 1267327A2 EP 02005056 A EP02005056 A EP 02005056A EP 02005056 A EP02005056 A EP 02005056A EP 1267327 A2 EP1267327 A2 EP 1267327A2
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Prior art keywords
synthesis
speech
speech sample
roots
coefficients
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EP02005056A
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English (en)
French (fr)
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EP1267327A3 (de
EP1267327B1 (de
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Khosrow Lashkari
Toshio Miki
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NTT Docomo Inc
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NTT Docomo Inc
Docomo Communications Labs USA Inc
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Priority claimed from US09/800,071 external-priority patent/US6859775B2/en
Priority claimed from US10/039,528 external-priority patent/US20030097267A1/en
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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
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L19/04—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques
    • G10L19/08—Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters
    • G10L19/12—Determination or coding of the excitation function; Determination or coding of the long-term prediction parameters the excitation function being a code excitation, e.g. in code excited linear prediction [CELP] vocoders
    • 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
    • G10L19/00—Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis
    • G10L2019/0001—Codebooks
    • G10L2019/0013—Codebook search algorithms

Definitions

  • the present invention relates generally to speech encoding, and more particularly, to an encoder and a gradient search algorithm.
  • Speech compression is a well known technology for encoding speech into digital data for transmission to a receiver which then reproduces the speech.
  • the digitally encoded speech data can also be stored in a variety of digital media between encoding and later decoding (i.e., reproduction) of the speech.
  • Speech synthesis systems differ from other analog and digital encoding systems that directly sample an acoustic sound at high bit rates and transmit the raw sampled data to the receiver.
  • Direct sampling systems usually produce a high quality reproduction of the original acoustic sound and is typically preferred when quality reproduction is especially important.
  • Common examples where direct sampling systems are usually used include music phonographs and cassette tapes (analog) and music compact discs and DVDs (digital).
  • One disadvantage of direct sampling systems is the large bandwidth required for transmission of the data and the large memory required for storage of the data. Thus, for example, in a typical encoding system which transmits raw speech sampled from the original acoustic sound, a data rate as high as 96,000 bits per second is often required.
  • speech synthesis systems use a mathematical model of the human speech production.
  • the fundamental techniques of speech modeling are known in the art and are described in B.S. Atal and Suzanne L. Hanauer, Speech Analysis and Synthesis by Linear Prediction of the Speech Wave, The Journal of the Acoustical Society of America 637-55 (vol. 50 1971).
  • the model of human speech production used in speech synthesis systems is usually referred to as a source-filter model.
  • this model includes an excitation signal that represents air flow produced by the vocal folds, and a synthesis filter that represents the vocal tract (i.e., the glottis, mouth, tongue, nasal cavities and lips). Therefore, the excitation signal acts as an input signal to the synthesis filter similar to the way the vocal folds produce air flow to the vocal tract.
  • the synthesis filter then alters the excitation signal to represent the way the vocal tract manipulates the air flow from the vocal folds.
  • the resulting synthesized speech signal becomes an approximate representation of the original speech.
  • speech synthesis systems One advantage of speech synthesis systems is that the bandwidth needed to transmit a digitized form of the original speech can be greatly reduced compared to direct sampling systems. Thus, by comparison, whereas direct sampling systems transmit raw acoustic data to describe the original sound, speech synthesis systems transmit only a limited amount of control data needed to recreate the mathematical speech model. As a result, a typical speech synthesis system can reduce the bandwidth needed to transmit speech to about 4,800 bits per second.
  • One problem with speech synthesis systems is that the quality of the reproduced speech is sometimes relatively poor compared to direct sampling systems. Most speech synthesis systems provide sufficient quality for the receiver to accurately perceive the content of the original speech. However, in some speech synthesis systems, the reproduced speech is not transparent. That is, while the receiver can understand the words originally spoken, the quality of the speech may be poor or annoying. Thus, a speech synthesis system that provides a more accurate speech production model is desirable.
  • a speech encoding system for optimizing the mathematical model of human speech production.
  • the speech synthesis system uses the LPC technique to compute coefficients of the synthesis filter.
  • the synthesis filter is then optimized by minimizing the synthesis error between the original speech and the synthesized speech.
  • the LPC coefficients are converted into roots of the synthesis filter.
  • a gradient search algorithm is then used to find the optimal roots. When the optimal roots are found, the roots are converted back into polynominal coefficients and are quantized for transmission.
  • This solution involves minimizing a synthesis error between an original speech sample and a synthesized speech sample.
  • One difficulty that was discovered is that speech synthesis systems are of a highly nonlinear nature with respect to the synthesis error, which made the problem previously mathematically intractable. This difficulty is overcome by solving the problem using the roots of the synthesis filter polynomial instead of the coefficients of the polynomial.
  • One root searching algorithm that may be used in such speech coders is a gradient search algorithm.
  • gradient search algorithms use an iterative solution process that calculates a gradient vector for a function and estimates the unknown variables using the calculated gradient vector.
  • an improved gradient search algorithm is provided.
  • the improved algorithm recalculates the gradient vector by taking into account the variations of the decomposition coefficients with respect to the roots.
  • the gradient search algorithm is especially useful with linear predictive coding speech systems that optimize synthesized speech by searching for roots of a polynomial.
  • a speech synthesis system is provided that minimizes synthesis filter errors in order to more accurately model the original speech.
  • a speech analysis-by-synthesis (AbS) system is shown which is commonly referred to as a source-filter model.
  • source-filter models are designed to mathematically model human speech production.
  • the model assumes that the human sound-producing mechanisms that produce speech remain fixed, or unchanged, during successive short time intervals (e.g., 20 to 30 ms).
  • the model further assumes that the human sound producing mechanisms change after each interval or between successive intervals.
  • the physical mechanisms modeled by this system include air pressure variations generated by the vocal folds, the glottis, the mouth, the tongue, the nasal cavities and the lips. Therefore, by limiting the digitally encoded data to a small set of control data for each interval, the speech decoder can reproduce the model and recreate the original speech. Thus, raw sampled data of the original speech is not transmitted from the encoder to the decoder. As a result, the digitally encoded data which is transmitted or stored (i.e., the bandwidth, or the number of bits) is much less than typical direct sampling systems require.
  • Fig. 1 shows an original digitized speech 10 delivered to an excitation module 12, thereby delivering an original speech sample s(n) to the excitation module 12.
  • the excitation module 12 analyzes each sample s(n) of the original speech and generates an excitation function u(n).
  • the excitation function u(n) is typically a series of pulse signals that represent air bursts from the lungs which are released by the vocal folds to the vocal tract.
  • the excitation function u(n) may be either a voiced 13, 14 or an unvoiced signal 15.
  • the excitation function u(n) has been treated as a preset series of pulses 13 with a fixed magnitude G and period P between the pitch pulses. As those in the art well know, the magnitude G and period P may vary between successive intervals. In contrast to the traditional fixed magnitude M and period P, it has previously been shown to the art that speech synthesis can be improved by optimizing the excitation function u(n) by varying the magnitude and pitch period of the excitation pulses 14. This improvement is described in Bishnu S. Atal and Joel R.
  • CELP Code-Excited Linear Prediction
  • the excitation module 12 can also generate an unvoiced 15 excitation function u(n).
  • An unvoiced 15 excitation function u(n) is used when the speaker's vocal folds are open and turbulent air flow is produced through the vocal tract.
  • Most excitation modules 12 model this state by generating an excitation function u(n) consisting of white noise 15 (i.e., a random signal) instead of pulses.
  • the synthesis filter 16 models the vocal tract and its effect on the air flow from the vocal folds.
  • the synthesis filter 16 uses a polynomial equation to represent the various shapes of the vocal tract. This technique can be visualized by imagining a multiple section hollow tube with a number of different diameters along the length of the tube. Accordingly, the synthesis filter 16 alters the characteristics of the excitation function u(n) similar to the way the vocal tract alters the air flow from the vocal folds, or like a variable diameter hollow tube alters inflowing air.
  • A(z) is a polynomial of order M and can be represented by the formula:
  • the order of the polynomial A(z) can vary depending on the particular application, but a 10th order polynomial is commonly used with an 8 kHz sampling rate.
  • the relationship of the synthesized speech s and(n) to the excitation function u(n) as determined by the synthesis filter 16 can be defined by the formula:
  • the coefficients a 1 ... a m of this polynomial have been computed using a technique known in the art as linear predictive coding (LPC).
  • LPC-based techniques compute the polynomial coefficients a 1 ... a M by minimizing the total prediction error e p .
  • the sample prediction error e p (n) is defined by the formula:
  • the total prediction error E p is then defined by the formula: where N is the length of the analysis window in number of samples.
  • the polynomial coefficients a 1 ... a M can now be resolved by minimizing the total prediction error E p using well known mathematical techniques.
  • the total synthesis error E s can then be defined by the formula: where N is the length of the analysis window.
  • the total synthesis error E s should be minimized to resolve the optimum filter coefficients a 1 ... a M .
  • the synthesized speech s and(n) as represented in formula (3) makes the total synthesis error E s a highly nonlinear function that is generally mathematically intractable.
  • A(z) (1- ⁇ 1 z -1 ) (1- ⁇ M z -1 ) where ⁇ 1 ... ⁇ M represent the roots of the polynomial A(z). These roots may be either real or complex. Thus, in the preferred 10th order polynomial, A(z) will have 10 different roots.
  • the synthesis filter function H(z) is now represented in terms of the roots by the formula: (the gain term G is omitted from this and the remaining formulas for simplicity).
  • decomposition coefficients b i are then calculated by the residue method for polynomials, thus providing the formula:
  • the impulse response h(n) can also be represented in terms of the roots by the formula:
  • a number of root searching algorithms may be used to minimize the total synthesis error E s .
  • ⁇ is the step size
  • ⁇ j E s is the gradient of the synthesis error E s relative to the roots at iteraton j.
  • the step size ⁇ can be either fixed for each iteration, or alternatively, it can be variable and adapted for each iteration.
  • the synthesis error gradient vector ⁇ j E s can now be calculated by the formula:
  • Formula (17) demonstrates that the synthesis error gradient vector ⁇ j E s can be calculated using the gradient vector of the synthesized speech samples s and(k).
  • the partial derivatives can be calculated by the formula: where ⁇ s and(0)/ ⁇ (j) / 1 is always zero.
  • the partial derivative ⁇ s and(k)/ ⁇ (j) / r can be calculated by the formula: (the superscript j is omitted from formula (19B) through formula (28) for notational simplicity).
  • K(i,r) 1/(1- ⁇ r ⁇ i -1 ) (if r ⁇ i)
  • K(i,r) 1/(1- ⁇ r ⁇ i -1 ) (if r ⁇ i)
  • the partial derivative of the k-th sample of the synthesized speech with respect to the r-th root can be expressed by the formula:
  • the partial derivative can be expressed by the formula:
  • the Iteration index can be inserted back into formula (28) to express the partial derivative of the synthesized speech at iteration j by the formula:
  • the synthesis error gradient vector ⁇ j E s is now calculated by substituting formula (29) into formula (18) and formula (18) into formula (17).
  • the subsequent root vector ⁇ (j+1) at the next iteration can then be calculated by substituting the result of formula (17) into formula (16).
  • the iterations of the gradient search algorithm are then repeated until either the synthesis error E s is reduced by a desired percentage from the LPC prediction error E p , a predetermined number of iterations are completed, or the roots are resolved within a predetermined acceptable range.
  • the synthesis error gradient vector ⁇ j E s is now calculated by substituting formula (19A) (for constant decomposition coefficients) or formula (29) (for varying decomposition coefficients) into formula (18) and formula (18) into formula (17).
  • the subsequent root vector ⁇ (j) at the next iteration can then be calculated by substituting the result of formula (17) into formula (16).
  • the iterations of the gradient search algorithm are then repeated until either the synthesis error gradient vector ⁇ j E s is reduced to a predetermined acceptable range - e.g., the synthesis error E S is reduced by a desired percentage from the LPC prediction error E p -, a predetermined number of iterations are completed, or the roots are resolved within a predetermined acceptable range.
  • control data for the optimal synthesis polynomial A(z) can be transmitted in a number of different formats, it is preferable to convert the roots found by the optimization technique described above back into polynomial coefficients a 1 ... a M .
  • the conversion can be performed by well known mathematical techniques. This conversion allows the optimized synthesis polynomial A(z) to be transmitted in the same format as in the existing speech encoding, thus promoting compatibility with current standards.
  • the control data for the model is quantized into digital data for transmission or storage.
  • the control data that is quantized includes ten synthesis filter coefficients a 1 ... a 10 , one gain value G for the magnitude of the excitation function pulses, one pitch period value P for the frequency of the excitation function pulses, and one indicator for a voiced 13 or unvoiced 15 excitation function u(n).
  • this example does not include an optimized excitation pulse 14, which could be included with some additional control data.
  • the described example requires the transmission of thirteen different variables at the end of each speech frame. Commonly, the thirteen variables are quantized into a total of 80 bits.
  • the synthesized speech s and(n), including optimization can be transmitted within a bandwidth of 4,000 bits/s (80 bits/frame ⁇ .020 s/frame).
  • the order of operations can be changed depending on the accuracy desired and the computing capacity available.
  • the excitation function u(n) was first determined to be a preset series of pulses 13 for voiced speech or an unvoiced signal 15.
  • the synthesis filter polynomial A(z) was determined using conventional techniques, such as the LPC method.
  • the synthesis polynomial A(z) was optimized.
  • the polynomial coefficients a 1 ... a M are first converted 34 to the roots of the polynomial A(z).
  • a gradient search algorithm is then used to optimize 38, 42, 44 the roots. Once the optimal roots are found, the roots are then converted 46 back to polynomial coefficients a 1 ... a M for compatibility with existing encoding-decoding systems.
  • the synthesis model and the index to the codebook entry is quantized 48 for transmission or storage.
  • FIG. 3 a flow chart of the gradient search algorithm is shown.
  • first roots of the polynominal are computed 50.
  • the initial roots may be determined by several methods, including root finding algorithms such as Newton-Raphson or interval halving.
  • Decomposition coefficients b l are then calculated using the first computed roots 52.
  • the gradient vector of the polynominal is calculated using the contribution of the decomposition coefficients b i 54.
  • the gradient vector is used to calculate second estimated roots 56.
  • a test is then performed to determine whether the search should end or whether it should continue 58.
  • Additional encoding sequences are also possible for improving the accuracy of the synthesis model or for changing the computing capacity needed to encode the synthesis model.
  • Some of these altemative sequences are demonstrated in Figure 1 by dashed routing lines.
  • the excitation function u(n) can be reoptimized at various stages during encoding of the synthesis model.
  • Figures 4-6 show the improved results provided by the optimized speech synthesis system assuming constant decomposition coefficients.
  • the figures show several different comparisons between a prior art LPC synthesis system and the optimized synthesis system.
  • the speech sample used for this comparison is a segment of a voiced part of the nasal "m".
  • Figure 4 a timeline-amplitude chart of the original speech, a prior art LPC synthesized speech and the optimized synthesized speech is shown. As can be seen, the optimally synthesized speech matches the original speech much closer than the LPC synthesized speech.
  • the reduction in the synthesis error is shown for successive iterations of optimization.
  • the synthesis error equals the LPC synthesis error since the LPC coefficients serve as the starting point for the optimization.
  • the improvement in the synthesis error is zero at the first iteration.
  • the synthesis error steadily decreases with each iteration.
  • the synthesis error increases (and the improvement decreases) at iteration number three. This characteristic occurs when the root searching algorithm overshoots the optimal roots. After overshooting the optimal roots, the search algorithm can be expected to take the overshoot into account in successive iterations, thereby resulting in further reductions in the synthesis error.
  • the synthesis error can be seen to be reduced by 37% after six iterations. Thus, a significant improvement over the LPC synthesis error is possible with the optimization.
  • Figure 6 shows a spectral chart of the original speech, the LPC synthesized speech and the optimized synthesized speech.
  • the first spectral peak of the original speech can be seen in this chart at a frequency of about 280 Hz. Accordingly, the optimized synthesized speech matches the spectral peak of the original speech at 280 Hz much closer than the LPC synthesized speech.
  • Figs. 7-9 show the improved results achieved in a speech synthesis system optimized according to such further improved gradient search algorithms.
  • the figures show several different comparisons between a prior art LPC synthesis system and the optimized synthesis system.
  • the speech sample used for this comparison is a segment of a voiced part of the nasal "m".
  • Fig. 7 a timeline-amplitude chart of the original speech, a prior art LPC synthesized speech and the optimized synthesized speech is shown. As can be seen, the optimally synthesized speech matches the original speech much closer than the LPC synthesized speech.
  • the reduction in the synthesis error is shown for successive iterations of optimization.
  • the synthesis error equals the LPC synthesis error since the LPC coefficients serve as the starting point for the optimization.
  • the improvement in the synthesis error is zero at the first iteration.
  • the synthesis error steadily decreases with each Iteration.
  • the synthesis error increases (and the improvement decreases) at iteration number three. This characteristic occurs when the root searching algorithm overshoots the optimal roots. After overshooting the optimal roots, the search algorithm can be expected to take the overshoot into account in successive iterations, thereby resulting in further reductions in the synthesis error.
  • the synthesis error can be seen to be reduced by 59% after six iterations. Thus, a significant improvement over the LPC synthesis error is possible with the optimization.
  • Figure 9 shows a spectral chart of the original speech, the LPC synthesized speech and the optimized synthesized speech.
  • the spectrum of the optimized speech provides a much better match to the spectrum of the original speech as compared to the LPC spectrum.
  • the improvement in the synthesized spectrum is especially apparent in the frequency range of 0 to 1,500 Hz.

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  • Engineering & Computer Science (AREA)
  • Computational Linguistics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
EP02005056A 2001-03-06 2002-03-06 Optimierung von Modellparametern zur Sprachkodierung Expired - Lifetime EP1267327B1 (de)

Applications Claiming Priority (4)

Application Number Priority Date Filing Date Title
US09/800,071 US6859775B2 (en) 2001-03-06 2001-03-06 Joint optimization of excitation and model parameters in parametric speech coders
US800071 2001-03-06
US10/039,528 US20030097267A1 (en) 2001-10-26 2001-10-26 Complete optimization of model parameters in parametric speech coders
US39528 2001-10-26

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EP1267327A2 true EP1267327A2 (de) 2002-12-18
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7200552B2 (en) * 2002-04-29 2007-04-03 Ntt Docomo, Inc. Gradient descent optimization of linear prediction coefficients for speech coders
US8386248B2 (en) 2006-09-22 2013-02-26 Nuance Communications, Inc. Tuning reusable software components in a speech application
CN112382266B (zh) * 2020-10-30 2024-08-02 北京有竹居网络技术有限公司 一种语音合成方法、装置、电子设备及存储介质

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
FRATTI M ET AL: "AN APPROACH TO PARAMETER REOPTIMIZATION IN MULTIPULSE-BASE CODERS" IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, IEEE INC. NEW YORK, US, vol. 1, no. 4, 1 October 1993 (1993-10-01), pages 463-465, XP000422860 ISSN: 1063-6676 *
LASHKARI K ET AL: "Joint optimization of model and excitation in parametric speech coders" PROCEEDINGS OF IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP'02), ORLANDO, FL, USA, vol. 1, 13 - 17 May 2002, pages 277-280, XP002275540 2002, Piscataway, NJ, USA, IEEE, USA ISBN: 0-7803-7402-9 *
RICHARDS J C ET AL: "A gradient-based variable step-size LMS algorithm" PROCEEDINGS OF SOUTHEASTCON. WILLIAMSBURG, APRIL 7 - 10, 1991, PROCEEDINGS OF THE SOUTHEAST CONFERENCE, NEW YORK, IEEE, US, vol. 1, 7 April 1991 (1991-04-07), pages 1083-1087, XP010045073 ISBN: 0-7803-0033-5 *
YINING CHEN ET AL: "A new algorithm for parameter re-optimization in multi-pulse excitation LP synthesizer" 2000 IEEE ASIA-PACIFIC CONFERENCE ON CIRCUITS AND SYSTEMS, 4 December 2000 (2000-12-04), pages 560-563, XP010537673 *

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DE60215420D1 (de) 2006-11-30
JP2002328692A (ja) 2002-11-15
JP2005099825A (ja) 2005-04-14
DE60215420T2 (de) 2007-08-30
EP1267327A3 (de) 2004-05-26
EP1267327B1 (de) 2006-10-18

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