EP0076233A1 - Procédé et dispositif pour traitement digital de la parole réduisant la redondance - Google Patents

Procédé et dispositif pour traitement digital de la parole réduisant la redondance Download PDF

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Publication number
EP0076233A1
EP0076233A1 EP82810390A EP82810390A EP0076233A1 EP 0076233 A1 EP0076233 A1 EP 0076233A1 EP 82810390 A EP82810390 A EP 82810390A EP 82810390 A EP82810390 A EP 82810390A EP 0076233 A1 EP0076233 A1 EP 0076233A1
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Prior art keywords
speech
energy
decision
test
signal
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EP82810390A
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German (de)
English (en)
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EP0076233B1 (fr
Inventor
Stephan Dr. Horvath
Yung-Shain Wu
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Omnisec AG Te Regensdorf Zwitserland
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Gretag AG
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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
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/93—Discriminating between voiced and unvoiced parts of speech signals
    • 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
    • G10L19/06—Determination or coding of the spectral characteristics, e.g. of the short-term prediction coefficients

Definitions

  • the invention relates to a redundancy-reducing digital speech processing method that works according to the method of linear prediction and to a corresponding device according to the preamble of patent claim 1 and patent claim 33.
  • the LPC vocoders known and available today are not yet fully satisfactory. Although the language synthesized again after the analysis is usually still relatively understandable, it is distorted and sounds artificial. One of the main reasons for this is above all in the difficulty of making the decision with certainty whether there is a voiced or an unvoiced speech section with sufficient certainty. Other causes include poor determination of the pitch period and inaccurate determination of the sound formation filter parameters.
  • the present invention is now primarily concerned with the first of these difficulties and aims to improve a digital speech processing method or system of the type defined at the outset in such a way that it makes more accurate or more reliable voiced-unvoiced decisions and thus an improvement in Quality of the synthesized language leads.
  • a number of decision criteria are known for the voiced-voiceless classification, which are used individually or in part in combination. Common criteria are e.g. the energy of the speech signal, the number of zero crossings of the same within a certain time period, the normalized residual error energy, i.e. the ratio of the energy of the prediction error signal to that of the speech signal, and the level of the second maximum of the autocorrelation function of the speech signal or of the prediction error signal. Furthermore, it is also common to carry out a cross-comparison to one or more neighboring language sections. A clear and comparative representation of the most important classification criteria and methods is e.g. the publication by L.R. Rabiner et al.
  • a common feature of all these known methods and criteria is that two-sided decisions are always made by definitely assigning the language section in each case to one or the other of the two options, depending on whether or not the relevant criteria are met. In this way, it can be achieved with a suitable selection and, if necessary, a combination of the decision criteria, a relatively high degree of accuracy, however, as practice shows, wrong decisions still occur relatively often, which significantly affect the quality of the synthesized language.
  • a main reason for this is the fact that speech signals are generally non-stationary in spite of all redundancy, because of which it is simply not possible to set the decision thresholds used in the respective criteria in such a way that a reliable statement can be made on both sides. A certain level of uncertainty always remains and must be accepted.
  • the invention now proceeds from this previously used principle of bilateral decisions and instead uses a strategy in which only one-sided, but practically absolutely safe decisions are made.
  • the locations of the respective decision thresholds are decisive for the degree of security of the individual decisions. The more extreme these decision thresholds are, the more selective the criteria and the safer the decisions. However, with increasing selectivity of the individual criteria, the number of the maximum necessary decision-making operations increases. In practice, however, it is easily possible to set the thresholds in such a way that practically absolute (one-sided) decision-making certainty is achieved without the total number of criteria or decision-making operations increasing above the level specified above.
  • this is from some source, e.g. analog voice signal originating from a microphone 1 band-limited in a filter 2 and then sampled and digitized in an A / D converter 3.
  • the sampling rate is about 6 to 16 kHz, preferably about 8 kHz.
  • the resolution is about 8 to 12 bit.
  • the pass band of the filter 2 usually extends from about 80 Hz to about 3.1-3.4 kHz in the case of so-called broadband speech, and from about 300 Hz to 3.1-3.4 kHz in the case of telephone speech.
  • the digital speech signal sn is divided into successive, preferably overlapping speech sections, so-called frames.
  • the speech section length can be about 10 to 30 msec, preferably about 20 msec. be.
  • the frame rate, ie the number of frames per second, is approximately 30 to 100, preferably approximately 45 to 70.
  • the analysis is essentially divided into two main procedures, firstly in the calculation of the amplification factor or volume parameter and the coefficients or filter parameters of the underlying vocal tract model filter and secondly in the voiced-unvoiced decision and in determining the pitch -Period in voiced case.
  • the filter coefficients are obtained in a parameter calculator 4 by solving the system of equations which is obtained when the energy of the prediction error, ie the energy of the difference between the actual samples and the samples estimated on the basis of the model assumption in the interval under consideration (speech section) is minimized as a function of the coefficients becomes.
  • the system of equations is preferably solved using the autocorrelation method using an algorithm according to Durbin (see, for example, LB Rabiner and RW Schafer "Digital Processing of Speech Signals", Prentice-Hall Inc., Englewood Cliffs, NJ 1978, pp. 411-413) .
  • the so-called reflection coefficients (k J ) also result, which are less sensitive transforms of the filter coefficients (aj) to quantization.
  • the reflection coefficients are always smaller than 1 and, in addition, their amount decreases with an increasing atomic number. Because of these advantages, the reflection coefficients (kj) are preferably transmitted instead of the filter coefficients (a j ).
  • the volume parameter G results from the algorithm as a by-product.
  • the digital speech signal s n is temporarily stored in a buffer 5 until the filter parameters (a.) Are calculated.
  • the signal then passes through an inverse filter 6 set with the parameters (a j ), which has an inverse transfer function to the transfer function of the vocal tract model filter.
  • the result of this inverse filtering is a prediction error signal e n , which is similar to the excitation signal x n multiplied by the gain factor G.
  • This prediction error signal e n is now supplied in the case of telephone speech directly or in the case of broadband speech via a low-pass filter 7 to an autocorrelation stage 8, which forms the autocorrelation function AKF standardized to the zero-order autocorrelation maximum, on the basis of which the pitch period p is determined in a pitch extraction stage 9, in a known manner Way as the distance of the second autocorrelation maximum RXX from the first maximum (zero order), preferably using an adaptive search method.
  • the low-pass filter 7 will be explained further below. At this point it should only be mentioned that it can be bridged by means of a switch 10 for telephone speech and could also be arranged in front of the inverse filter 6.
  • the speech section under consideration is classified as voiced or unvoiced according to the decision procedure according to the invention to be explained in more detail in a decision stage 11 which is supported by an energy determination stage 12 and a zero crossing determination stage 13.
  • the pitch parameter p is set to zero.
  • the parameter calculator described above determines a set of filter parameters for each speech section (frame).
  • the filter parameters could also be determined differently, for example continuously by means of adaptive inverse filtering or another known method, the filter parameters being readjusted continuously with each sampling cycle, but only at the times determined by the frame rate for further processing or Transmission will be provided.
  • the invention is in no way restricted in this regard. It is only essential that there is a set of filter parameters for each language section.
  • the speech signal is recovered or synthesized from the parameters in a known manner in that the parameters initially decoded in a decoder 15 are fed to a pulse-noise generator 16, an amplifier 17 and a vocal tract model filter 18 and the output signal of the model filter 18 by means of a D / A converter 19 is brought into analog form and then made audible after the usual filtering 20 by a playback device, for example a loudspeaker 21.
  • the volume parameter G controls the amplification factor of the amplifier 17, the filter parameters (kj) define the transfer function of the sound-forming or vocal tract model filter 18.
  • Fig. 2 An example of such a system is shown in Fig. 2 as a block diagram.
  • the multi-processor system shown essentially comprises four functional blocks, namely a main processor 50, two secondary processors 60 and 70 and an input / output unit 80. It implements both analysis and synthesis.
  • the input / output unit 80 contains the stages designated 81 for analog signal processing, such as amplifiers, filters and automatic gain control, as well as the A / D converter and the D / A converter.
  • the main processor 50 carries out the actual speech analysis or synthesis, for which purpose the determination of the filter parameters and the volume parameters (parameter calculator 4), the determination of energy and zero crossings of the speech signal (stages 12 and 13), the voiced-unvoiced decision (stage 11 ) and the determination of the pitch period (stage 9) or synthesis-side the generation of the output signal (stage 16), its volume variation (stage 17) and its filtering in the speech model filter (filter 18).
  • the main processor 50 is supported by the secondary processor 60, which carries out the intermediate storage (buffer 5), inverse filtering (stage 6), optionally the low-pass filtering (stage 7) and the autocorrelation (stage 8).
  • the secondary processor 70 deals exclusively with the coding or decoding of the speech parameters and with the data traffic with e.g. a modem 90 or the like via an interface designated 71.
  • the voiced-unvoiced decision-making procedure is explained in more detail below.
  • the determination of the pitch period is preferably based on a longer analysis interval than for the determination of the filter coefficients.
  • the analysis interval is the same as the language section under consideration; for pit extraction, on the other hand, the analysis interval extends on both sides of the language section into the respectively adjacent language section, for example up to about half of the same. In this way, a more reliable and less erratic pitch extraction can be carried out.
  • the energy of a signal is referred to in the following, this always means the relative energy of the signal in the analysis interval, that is to say standardized to the dynamic range of the A / D converter 3.
  • FIG. 3 and 4 show the flow diagrams of two particularly expedient decision-making processes according to the invention, specifically in FIG. 3 a variant for broadband voice and in FIG. 4 such a variant for telephone voice.
  • an energy test is carried out as the first decision criterion.
  • the (relative, standardized) energy E s of the speech signal s is compared with a minimum energy threshold EL, which is set so low that the speech section can certainly be called unvoiced if the energy Es is not above this threshold.
  • Practical values for this minimum energy threshold EL are 1.1 x 10 to 1.4 x 10 -4 , preferably about 1.2 x 10 -4 .
  • the next criterion is a zero-crossing test.
  • the number of zero crossings of the digital voice signal is determined in the analysis interval and compared with a maximum number of ZCU. If the number is greater than this maximum number, the language section is clearly rated as unvoiced, otherwise a further decision criterion is used.
  • the maximum number ZCU is approximately 105 to 120, preferably approximately 110 zero crossings for an analysis interval length of 256 samples.
  • the next decision criterion is the normalized autocorrelation function AFK of the low-pass filtered prediction error signal e n , namely the normalized autocorrelation maximum RXK, which is at a distance from the zero-order order identified by the IndeX IP, is compared with a threshold value RU and evaluated as correct if this threshold is exceeded. Otherwise, the next criterion is advanced. Practically favorable values for the threshold are 0.55 to 0.75, preferably about 0.6.
  • the energy of the low-pass filtered prediction error signal e is examined. If this energy ratio V is less than a 0 first, lower ratio threshold VL, the speech section is rated as voiced. Otherwise there is a further comparison with a second, higher ratio threshold VU, the decision being made unvoiced if the energy ratio V o is above this higher threshold VU. This second comparison may also be omitted.
  • Suitable values for the two ratio thresholds VL and VU are 0.05 to 0.15 and 0.6 to 0.75, preferably about 0.1 and 0.7.
  • the autocorrelation maximum RXX is first compared with a second, lower threshold value RM. If this threshold is exceeded, the decision will be made by voice. Otherwise, a cross-comparison with the two (possibly also only one) immediately preceding language sections is carried out as the last criterion. The speech section is only rated as unvoiced if the (or one) of the two previous speech sections were also unvoiced. Otherwise, a final decision will be made by voices. Suitable values for the threshold value RM are 0.35 to 0.45, preferably approximately 0.42.
  • the prediction error signal e n is low-pass filtered in broadband speech.
  • This low-pass filtering causes the frequency distributions of the autocorrelation maximum values to be split between two unvoiced and voiced speech sections and thus makes it easier to determine the decision threshold at the same time reducing the frequency of errors. It also enables better pitch extraction, ie determining the pitch period.
  • an essential condition for this is that the low-pass filtering is carried out with an extremely high slope of approx. 150 to 180 db / octave.
  • the (digital) filter used should have an elliptical characteristic, the cut-off frequency should be in the range of 700-1200 Hz, preferably 800 to 900 Hz.
  • the decision process for telephone speech shown in FIG. 4 largely corresponds to that for broadband speech. Only the sequence of the second energy test and the second zero-crossing test is reversed (not mandatory) and the second test of the auto-correlation maximum RXX is also omitted, since this would not work for telephone speech.
  • the individual decision thresholds are partly different, depending on the differences between the telephone language and the broadband language. Practical values are shown in the table below. With the two decision processes described above, a voiced-unvoiced decision was achieved with extremely small error rates. It goes without saying that the order of the criteria and the criteria themselves could in principle also be different, the only important thing is that only reliable decisions are made for each criterion.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Audiology, Speech & Language Pathology (AREA)
  • Computational Linguistics (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Human Computer Interaction (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Spectroscopy & Molecular Physics (AREA)
  • Compression, Expansion, Code Conversion, And Decoders (AREA)
  • Exchange Systems With Centralized Control (AREA)
  • Transmission Systems Not Characterized By The Medium Used For Transmission (AREA)
  • Use Of Switch Circuits For Exchanges And Methods Of Control Of Multiplex Exchanges (AREA)
  • Error Detection And Correction (AREA)
EP82810390A 1981-09-24 1982-09-20 Procédé et dispositif pour traitement digital de la parole réduisant la redondance Expired EP0076233B1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AT82810390T ATE15563T1 (de) 1981-09-24 1982-09-20 Verfahren und vorrichtung zur redundanzvermindernden digitalen sprachverarbeitung.

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CH6167/81 1981-09-24
CH616781 1981-09-24

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EP0076233A1 true EP0076233A1 (fr) 1983-04-06
EP0076233B1 EP0076233B1 (fr) 1985-09-11

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EP82810390A Expired EP0076233B1 (fr) 1981-09-24 1982-09-20 Procédé et dispositif pour traitement digital de la parole réduisant la redondance

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US (1) US4589131A (fr)
EP (1) EP0076233B1 (fr)
JP (1) JPS5870299A (fr)
AT (1) ATE15563T1 (fr)
CA (1) CA1184657A (fr)
DE (1) DE3266204D1 (fr)

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NL8400728A (nl) * 1984-03-07 1985-10-01 Philips Nv Digitale spraakcoder met basisband residucodering.
US5208861A (en) * 1988-06-16 1993-05-04 Yamaha Corporation Pitch extraction apparatus for an acoustic signal waveform
US4972474A (en) * 1989-05-01 1990-11-20 Cylink Corporation Integer encryptor
IT1229725B (it) * 1989-05-15 1991-09-07 Face Standard Ind Metodo e disposizione strutturale per la differenziazione tra elementi sonori e sordi del parlato
US5680508A (en) * 1991-05-03 1997-10-21 Itt Corporation Enhancement of speech coding in background noise for low-rate speech coder
US5280525A (en) * 1991-09-27 1994-01-18 At&T Bell Laboratories Adaptive frequency dependent compensation for telecommunications channels
US5361379A (en) * 1991-10-03 1994-11-01 Rockwell International Corporation Soft-decision classifier
FR2684226B1 (fr) * 1991-11-22 1993-12-24 Thomson Csf Procede et dispositif de decision de voisement pour vocodeur a tres faible debit.
JP2746033B2 (ja) * 1992-12-24 1998-04-28 日本電気株式会社 音声復号化装置
US5471527A (en) 1993-12-02 1995-11-28 Dsc Communications Corporation Voice enhancement system and method
TW271524B (fr) * 1994-08-05 1996-03-01 Qualcomm Inc
US5970441A (en) * 1997-08-25 1999-10-19 Telefonaktiebolaget Lm Ericsson Detection of periodicity information from an audio signal
US6381570B2 (en) * 1999-02-12 2002-04-30 Telogy Networks, Inc. Adaptive two-threshold method for discriminating noise from speech in a communication signal
US6980950B1 (en) * 1999-10-22 2005-12-27 Texas Instruments Incorporated Automatic utterance detector with high noise immunity
GB2357683A (en) * 1999-12-24 2001-06-27 Nokia Mobile Phones Ltd Voiced/unvoiced determination for speech coding
KR101008022B1 (ko) * 2004-02-10 2011-01-14 삼성전자주식회사 유성음 및 무성음 검출방법 및 장치
WO2009069662A1 (fr) * 2007-11-27 2009-06-04 Nec Corporation Système de détection de parole, procédé de détection de parole et programme de détection de parole
DE102008042579B4 (de) * 2008-10-02 2020-07-23 Robert Bosch Gmbh Verfahren zur Fehlerverdeckung bei fehlerhafter Übertragung von Sprachdaten
CN101859568B (zh) * 2009-04-10 2012-05-30 比亚迪股份有限公司 一种语音背景噪声的消除方法和装置
US9454976B2 (en) 2013-10-14 2016-09-27 Zanavox Efficient discrimination of voiced and unvoiced sounds
CN112885380B (zh) * 2021-01-26 2024-06-14 腾讯音乐娱乐科技（深圳）有限公司 一种清浊音检测方法、装置、设备及介质

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Also Published As

Publication number Publication date
ATE15563T1 (de) 1985-09-15
EP0076233B1 (fr) 1985-09-11
DE3266204D1 (en) 1985-10-17
US4589131A (en) 1986-05-13
JPS5870299A (ja) 1983-04-26
CA1184657A (fr) 1985-03-26

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