CA1336208C - Adaptive threshold voiced detector - Google Patents
Adaptive threshold voiced detectorInfo
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- CA1336208C CA1336208C CA000562765A CA562765A CA1336208C CA 1336208 C CA1336208 C CA 1336208C CA 000562765 A CA000562765 A CA 000562765A CA 562765 A CA562765 A CA 562765A CA 1336208 C CA1336208 C CA 1336208C
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- G—PHYSICS
- G10—MUSICAL INSTRUMENTS; ACOUSTICS
- G10L—SPEECH ANALYSIS OR SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING; 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
Abstract
Apparatus for detecting a fundamental frequency in speech by statistically analyzing a discriminant variable generated by a discriminant voiced detector (102) so as to determine the presence of the fundamental frequency in achanging speech environment. A statistical calculator (103) is responsive to thediscriminant variable to first calculate the average of all of the values of thediscriminant variable over the present and past speech frames and then to determine the overall probability that any frame will be unvoiced. In addition, the calculator informs two values, one value represents the statistical average of discriminant values that an unvoiced frame's discriminant variable would have and the other value represents the statistical average of the discriminant values for voice frames. These latter calculations are performed utilizing not only the average discriminant value but also a weight value and a threshold value which are adaptively determined by a threshold calculator (104) from frame to frame.
An unvoiced/voiced determinator (105) makes the unvoiced/voiced decision by utilizing the weight and threshold values. (FIG. 1)
An unvoiced/voiced determinator (105) makes the unvoiced/voiced decision by utilizing the weight and threshold values. (FIG. 1)
Description
-~ 336208 AN ADAPTIVE THRESHOLD VOICED DETECTOR
Technical Field This invention relates to determining whether or not speech contains a fi~nd~m.ont~l frequency which is commonly referred to as the unvoiced/voiced decision. More particularly, the unvoiced/voiced decision is made by a two stage5 voiced detector with the final threshold values being adaptively calculated for the speech environllRIlt utilizing statistical techniques.
Background and Problem In low bit rate voice coders, degradation of voice quality is often due to inaccurate voicing decisions. The difficulty in correctly making these voicing 10 decisions lies in the fact that no single speech parameter or classifier can reliably distinguish voiced speech from unvoiced speech. In order to make the voice decision, it is known in the art to combine multiple speech classifiers in the form of a weighted sum. This method is commonly called discrimin~nt analysis. Such a method is illustrated in D. P. Prezas, et al., "Fast and Accurate Pitch Detection 15 Using Pattern Recognition and Adaptive Time-Domain Analysis," Proc. IEEE Int.Conf. Acoust., Speech and Signal Proc., Vol. 1, pp. 109-112, April 1986. As described in that article, a frame of speech is declared voice if a weighted sum of cl~sifiers is greater than a specified threshold, and unvoiced otherwise. The weights and threshold are chosen to maximize pe.rw~ ce on a training set of 20 speech where the voicing of each frame is known.
A problem associated with the fixed weighted sum method is that it does not p~.rOl,l, well when the speech envin~nmellt changes. The reason is thatthe threshold is ~etermined from the training set which is dirf~ ;nt from speechsubject to background noise, non-linear distortion, and filtering One method for adapting the threshold value to changing speech en~ nlllent is disclosed in the paper of H. ~s~nein, et al., "Implementation of the Gold-Rabiner Pitch Detector in a Real Time Environment Using an Improved Voicing Detector," EEE Transactions on Acoustic, Speech and Signal Processing, 1986, Tokyo, Vol. ASSP-33, No. 1, pp. 319-320. This paper discloses 30 an empirical method which compares three dirr~,re.-t paldll~ete.~ against independent thresholds associated with these pa,allle~ and on the basis of each comparison either incl~lllenls or decrements by one an adaptive threshold value.The three pa,dllle~ utilized are energy of the signal, first reflection coefficient, and zero-crossing count. For example, if the energy of the speech signal is lessthan a predefined energy level, the adaptive threshold is incremented. On the other hand, if the energy of the speech signal is greater than another predefined energy level, the adaptive threshold is decremented by one. After the adaptive threshold 5 has been calculated, it is subtracted from an output of an elementary pitch detector.
If the results of the subtraction yield a positive number, the speech frame is declared voice; otherwise, the speech frame is declared on unvoice. The problem with the disclosed method is that the parameters themselves are not used in the elementary pitch detector. Hence, the adjustment of the adaptive threshold is 10 ad hoc and is not directly linked to the physical phenomena from which it is calculated. In addition, the threshold cannot adapt to rapidly ch~nging speech environments.
Solution In accordance with one aspect of the invention there is provided an 15 apparatus for detecting the presence of a fundamental frequency in frames of speech, comprising: means responsive to a set of classifiers defining speech attributes of one of said frames of speech for generating a general value indicating said presence of said fundamental frequency; means responsive to said general value for calculating a set of statistical parameters; means for calculating a 20 threshold value in response to said set of said parameters; means for calculating a weight value in response to said set of said parameters; means for communicatingsaid weight value and said threshold value to said means for calculating said set of parameters to be used for calculating another set of parameters for another one of said frames of speech; and means responsive to said weight value and said 25 threshold value and the calculated set of statistical parameters for determining said presence of said fundamental frequency in said present one of said frames of speech.
In accordance with another aspect of the invention there is provided a method for detecting the presence of a fundamental frequency in frames of speech30 comprising the steps of: generating a general value in response to a set of classifiers defining speech attributes of one of said frames of speech to indicate , -- -2a- 1 336208 !
said presence of said fundamental frequency; calculating a set of statistical parameters in response to said general value; and deterrnining said presence of said fundamental frequency in said one of said frames; said step of determining comprises the steps of calculating a threshold value in response to said set of said 5 parameters; calculating a weight value in response to said set of said parameters;
and communicating said weight value and said threshold value to said means for calculating said set of parameters to be used for calculating another set of parameters for another one of said frames of speech.
Advantageously, in response to speech attributes of the present and past 10 speech frames, the mean for unvoiced frames is calculated by calculating the probability that the present speech frame is unvoiced, calculating the overall probability that any frame will be unvoiced, and calculating the probability that the present speech frame is voiced. The mean of the unvoiced speech frames is then calculated in response to the probability that the present speech frame is unvoiced 15 and the overall probability. In addition, the mean of the voiced speech frame is calculated in response to the probability that the present speech frame is voiced and the overall probability. Advantageously, the calculations of probabilities are performed ~ltili7.ing a maximum likelihood statistical operation.
,~
Advantageously, the generation of the general value is performed utilizing a discriminant analysis procedure, and the speech attributes are speech classifiers.
Advantageously, the decision regions are de_ned by the mean of the 5 unvoiced and voiced speech frames and a weight and threshold value generated in response to the general values of past and present frames and the means of the voiced and unvoiced frames.
The method for detecting the presence of a fun~l~m~nt~l frequency in speech frames compri~es the steps of: generating a general value in response to a 10 set of cl~sifiers ~lefining speech attributes of a present speech frame to indic~t~
the presence of the filn~1~ment~1 frequency, calculating a set of statistical pa~ letel~ in response to the general value, and determining the presence of thefilnd~ment~l frequency in response to the general value and the calculated set of statistical pal~lRt~ . The step of generating the general value is p~,lrolllled 15 utilizing a discrimin~nt analysis procedure. Further, the step of dele ..~ ing the filnfl~mt~nt~l frequency comprises the step of calculating a weight and a threshold value in response to the set of parameters.
Brief Description of the Drawin~
FIG. 1 illusllales, in block diagram form, the present invention; and FIGS. 2 and 3 illustrate, in greater detail, certain functions pelrc~llcd by the voiced detection appa~alus of FIG. 1.
Detailed Description FIG. 1 illustrates an appalalus for pclr IlllPillg the unvoiced/voiced decision operation by _rst l1tili7ing a ~i~crimin~nt voiced detector to process voice 25 classifiers in order to generate a rliscrimin~nt variable or general variable. The latter variable is st~ti~tic~lly analyzed to make the voicing decision. The statistical analysis adapts the threshold utilized in m~king the unvoiced/voiceddecision so as to give reliable pelrollllance in a variety of voice enviro~
Consider now the overall operation of the al~a dlus illustrated in 30 FIG. 1. Cl~csifier gcnclalol 100 is responsive to each frame of voice to generate classifiers which advantageously may be the log of the speech energy, the log ofthe LPC gain, the log area ratio of the first reflection coefficient, and the squared correlation coefficient of two speech segments one frame long which are offset by one pitch period. The calculation of these classifiers involves digitally sampling 35 analog speech, forming frames of the digital samples, and processing those frames - 4 - l 3 3 6 2 0 ~
and is well known in the art. Generator 100 transmits the classifiers to silencedetector 101 and discriminant voiced detector 102 via path 106. Discrimin~nt voiced detector 102 is responsive to the classifiers received via path 106 to calculate the discrimin~nt value, x. Detector 102 pclrOllllS that calculation by5 solving the equation: x = c'y+d. Advantageously, "c" is a vector comprising the weights, "y" is a vector comprising the classifiers, and "d" is a scalar representing a threshold value. Advantageously, the components of vector c are inih~li7~-i asfollows: coll~ol-ent corresponding to log of the speech energy equals 0.3918606,colllpol1ellt cwl~ onding to log of the LPC gain equals -0.0520902, colllponent corresponding to log area ratio of the first reflection coefficient equals 0.5637082, and component corresponding to squared correlation coefficient equals 1.361249;
and d initially equals -8.36454. After calculating the value of the discrimin~ntvariable x, the detector 102 transmits this value via path 111 to statistical calculator 103 and subtracter 107.
Silence detector 101 is responsive to the cl~csifiers tr~nsmittçd via path 106 to determine whether speech is actually present on the data being received on path 109 by classifier generator 100. The intlic~tion of the presence of speech is tr~n~mitted via path 110 to statistical calculator 103 by silence detector 101.
For each frame of speech, detector 102 generates and transmits the discrimin~nt value x via path 111. St~tistic~l calculator 103 m~int~in~ an average of the ~ çrimin~nt values received via path 111 by averaging in the fli.~crimin~nt value for the present, non-silence frame with the ~liscrimin~nt values for previous non-silence frames. St~ti~tic~l calculator 103 is also responsive to the signal 25 received via path 110 to calculate the overall probability that any frame is unvoiced and the probability that any frame is voiced. In addition, st~ti~tic~l calculator 103 calculates the statisdcal value that the discrimin~nt value for the present frame would have if the frame was unvoiced and the statistical value that the discrimin~nt value for the present frame would have if the frame was voiced.30 Advantageously, that st~tistic~l value may be the mean. The calculations p~lrcllllcd by calculator 103 are not only based on the present frame but on previous frames as well. St~ti~ti-~l calculator 103 performs these calculations not only on the basis of the discrimin~nt value received for the present frame via path 106 and the average of the classifiers but also on the basis of a weight and a 35 threshold value defining whether a frame is unvoiced or voiced received via -path 113 from threshold calculator 104.
Calculator 104 is responsive to the probabilities and statistical values of the classifiers for the present frame as generated by calculator 103 and received via path 112 to recalculate the values used as weight value a, and threshold value S b for the present frame. Then, these new values of a and b are tr~nsmitted back to st~tisti-~l calculator 103 via path 113.
Calculator 104 transmits the weight, threshold, and statistical values via path 114 to U/V determin:ltor 105. The latter detector is responsive to the inîo~ ation transmitted via paths 114 and 115 to determine whether or not the 10 frame is unvoiced or voiced and tO LI~lSll~it this decision via path 116.
Consider now in greater detail the operations of blocks 103, 104, 105, and 107 illustrated in FIG. 1. Statistical calculator 103 implements an improvedEM algoliLhm similar to that suggested in the article by N. E. Day entitled ~F.stim~ting the Col~onenls of a Mixture of Normal Distributions", Biometrika, 15 Vol. 56, No. 3, pp. 463-474, 1969. Utilizing the concept of a decaying average, calculator 103 calculates the average for the discrimin~nt values for the present and previous frames by calculating following equations 1, 2, and 3:
n=n+l if n<2000 (1) z = l/n (2) Xn = (1--z) Xn--l + zxn (3) Xn is the discrimin~nt value for the present frame and is received from detector 102 via path 111, and n is the number of frames that have been processed up to 2000. z represents the decaying average coefflcient, and Xn represents the -- 6 - l 3 3 6 2 0 8 average of the discrimin~nt values for the present and past frames. Statistical calculator 103 is responsive to receipt of the z, xn and Xn values to calculate the variance value, T, by first calculating the second moment of Xn, Qn, as follows:
Qn = (l~z)Qn-l + zxn (4) S After Qn has been calculated, T is calculated as follows:
T = Qn--Xn (S) The mean is subtracted from the discrimin~nt value of the present frame as follows:
xn = xn -- Xn (6) 10 Next, calculator 103 determines the probability that the frame represented by the present value xn is unvoiced by solving equation 7 shown below:
P(ulx ) 1 (7) After solving equation 7, c~lcul~tor 103 determines the probability that the discrimin~nt value represents a voiced frame by solving the following:
P(vlxn) = 1--P(ulxn) . (8) -7 l 336208 Next, calculator 103 determines the overall probability that any frame will be unvoiced by solving equation 9 for Pn:
Pn = (1--z) Pn-l + z P(u I xn) . (9) After determining the probability that a frame will be unvoiced, S calculator 103 determines two values, u and v, which give the mean values of discrimin~nt value for both unvoiced and voiced type frames. Value u, statistical average unvoiced value, contains the mean discrimin~nt value if a frarne is unvoiced, and value v, st~ti~ti-~l average voiced value, gives the mean discrimin~nt value if a frame is voiced. Value u for the present frame is solved10 by calculating equation 10, and value v is determined for the present frame by calculating equationlll as follows:
Un = (1--z) Un_l + Z Xn P(ulxn)lpn--ZXn (10) Vn = ( 1--Z) Vn_l + Z Xn P(v I Xn) / ( 1--Pn) --ZXn ( 1 1 ) Calculator 103 now co...n~ ic~tes the u, v, and T values, and probability Pn to 15 threshold calculator 104 via path 112.
Calculator 104 is responsive to this info~ alion to calculate new values for a and b. These new values are then tr~n~mitte~l back to statistical calculator 103 via path 113. This allows rapid adaptations to changing enviro~ e -l~. If n is greater than advantageously 99, values a and b are 20 calculated as follows. Value a is determined by solving the following equa~ion:
a = rl (vn--un) (12) 1--Pn(l--pn) I--1 (un--Vn)2 Value b is determined by solving the following equation:
b =--2 a(un+vn) + log[(l-pn)/pn ] (13) After calculating equations 12 and 13, calculator 104 transmits values a, u, and v to block lOS via path 114.
Determinator 105 is responsive to this tr~n~mitte~ fc.~ ation to decide whether the present frame is voiced or unvoiced. If the value a is positive, then, a frame is declared voiced if the following equation is true:
axn--a(un+vn)/2 > 0; (14) or if the value a is negative, then, a frame is declared voiced if the following10 equation is true:
axn--a(un+vn)/2 < 0 . (15) Equation 14 can also be expressed as:
axn + b--log[(l -Pn) /Pn] >
Equation lS can also be expressed as:
axn + b--log[(l--Pn)/Pn] < --If the previous conditions are not met, determinator 105 declares the frame unvoiced.
In flow chart form, FIGS. 2 and 3 illustrate, in greater detail, the operations performed by the apparatus of FIG. 1. Block 200 implements 5 block 101 of FIG. 1. Blocks 202 through 218 implement statistical calculator 103. Block 222 implements threshold calculator 104, and blocks 226 through 239 implement block 105 of FIG. 1. Subtracter 107 is implemented by both block 208 and block 224. Block 202 calculates the value which represents the average of the discrimin~nt value for the present frame and all previous 10 frames. Block 200 determines whether speech is present in the present frame; and if speech is not present in the present frame, the mean for the discrimin~nt value is subtracted from the present discrimin~nt value by block 224 before control istransferred to decision block 226.
However, if speech is present in the present frame, then the statistical 15 and weight calculations are pelrwmed by blocks 202 through 222. First, the average value is found in block 202. Second, the second moment value is calculated in block 206. The latter value along with the mean value X for the present and past frames is then utilized to calculate the variance, T, also in block 206. The mean X is then subtracted from the discrimin~nt value xn in 20 block 208.
Block 210 calculates the probability that the present frame is unvoiced by utilizing the present weight value a, the present threshold value b, and the discrimin~nt value for the present frame, xn. After calculating the probability that the present frame is unvoiced, the probability that the present frame is voiced is 25 calculated by block 212. Then, the overall probability, Pn, that any frame will be unvoiced is calculated by block 214.
Blocks 216 and 218 calculate two values: u and v. The value u represents the st~tictin~l average value that the discrimin~nt value would have if the frame were unvoiced. Whereas, value v l~ipl~_sents the st~tisti~l average value 30 that the discrimin~nt value would have if the frame were voiced. The actual discrimin~nt values for the present and previous frames are clu~ ,d around either value u or value v. The discr~min~nt values for the previous and present frames are clustered around value u if these frames had been found to be unvoiced;
otherwise, the previous values are clustered around value v. Block 222 then 35 calculates a new weight value a and a new threshold value b. The values a and b - lO- 1 336208 are used in the next sequential frame by the prece~ling blocks in FIG. 2.
Blocks 226 through 239 implement U/V determinator 105 of FIG. 1.
Block 226 determines whether the value a for the present frame is greater than zero. If this condition is true, then decision block 228 is executed. The latterS decision block determines whether the test for voiced or unvoiced is met. If the frame is found to be voiced in decision block 228, then the frame is so marked as voiced by block 230 otherwise the frame is marked as unvoiced by block 232. If the value a is less than zero for the present frame, blocks 234 through 238 are executed and function in a similar manner to blocks 228 through 232.
It is to be understood that the afore-described embodiment is merely illustrative of the principles of the invention and that other arrangements may be devised by those skilled in the art without departing from the spirit and the scope of the invention.
Technical Field This invention relates to determining whether or not speech contains a fi~nd~m.ont~l frequency which is commonly referred to as the unvoiced/voiced decision. More particularly, the unvoiced/voiced decision is made by a two stage5 voiced detector with the final threshold values being adaptively calculated for the speech environllRIlt utilizing statistical techniques.
Background and Problem In low bit rate voice coders, degradation of voice quality is often due to inaccurate voicing decisions. The difficulty in correctly making these voicing 10 decisions lies in the fact that no single speech parameter or classifier can reliably distinguish voiced speech from unvoiced speech. In order to make the voice decision, it is known in the art to combine multiple speech classifiers in the form of a weighted sum. This method is commonly called discrimin~nt analysis. Such a method is illustrated in D. P. Prezas, et al., "Fast and Accurate Pitch Detection 15 Using Pattern Recognition and Adaptive Time-Domain Analysis," Proc. IEEE Int.Conf. Acoust., Speech and Signal Proc., Vol. 1, pp. 109-112, April 1986. As described in that article, a frame of speech is declared voice if a weighted sum of cl~sifiers is greater than a specified threshold, and unvoiced otherwise. The weights and threshold are chosen to maximize pe.rw~ ce on a training set of 20 speech where the voicing of each frame is known.
A problem associated with the fixed weighted sum method is that it does not p~.rOl,l, well when the speech envin~nmellt changes. The reason is thatthe threshold is ~etermined from the training set which is dirf~ ;nt from speechsubject to background noise, non-linear distortion, and filtering One method for adapting the threshold value to changing speech en~ nlllent is disclosed in the paper of H. ~s~nein, et al., "Implementation of the Gold-Rabiner Pitch Detector in a Real Time Environment Using an Improved Voicing Detector," EEE Transactions on Acoustic, Speech and Signal Processing, 1986, Tokyo, Vol. ASSP-33, No. 1, pp. 319-320. This paper discloses 30 an empirical method which compares three dirr~,re.-t paldll~ete.~ against independent thresholds associated with these pa,allle~ and on the basis of each comparison either incl~lllenls or decrements by one an adaptive threshold value.The three pa,dllle~ utilized are energy of the signal, first reflection coefficient, and zero-crossing count. For example, if the energy of the speech signal is lessthan a predefined energy level, the adaptive threshold is incremented. On the other hand, if the energy of the speech signal is greater than another predefined energy level, the adaptive threshold is decremented by one. After the adaptive threshold 5 has been calculated, it is subtracted from an output of an elementary pitch detector.
If the results of the subtraction yield a positive number, the speech frame is declared voice; otherwise, the speech frame is declared on unvoice. The problem with the disclosed method is that the parameters themselves are not used in the elementary pitch detector. Hence, the adjustment of the adaptive threshold is 10 ad hoc and is not directly linked to the physical phenomena from which it is calculated. In addition, the threshold cannot adapt to rapidly ch~nging speech environments.
Solution In accordance with one aspect of the invention there is provided an 15 apparatus for detecting the presence of a fundamental frequency in frames of speech, comprising: means responsive to a set of classifiers defining speech attributes of one of said frames of speech for generating a general value indicating said presence of said fundamental frequency; means responsive to said general value for calculating a set of statistical parameters; means for calculating a 20 threshold value in response to said set of said parameters; means for calculating a weight value in response to said set of said parameters; means for communicatingsaid weight value and said threshold value to said means for calculating said set of parameters to be used for calculating another set of parameters for another one of said frames of speech; and means responsive to said weight value and said 25 threshold value and the calculated set of statistical parameters for determining said presence of said fundamental frequency in said present one of said frames of speech.
In accordance with another aspect of the invention there is provided a method for detecting the presence of a fundamental frequency in frames of speech30 comprising the steps of: generating a general value in response to a set of classifiers defining speech attributes of one of said frames of speech to indicate , -- -2a- 1 336208 !
said presence of said fundamental frequency; calculating a set of statistical parameters in response to said general value; and deterrnining said presence of said fundamental frequency in said one of said frames; said step of determining comprises the steps of calculating a threshold value in response to said set of said 5 parameters; calculating a weight value in response to said set of said parameters;
and communicating said weight value and said threshold value to said means for calculating said set of parameters to be used for calculating another set of parameters for another one of said frames of speech.
Advantageously, in response to speech attributes of the present and past 10 speech frames, the mean for unvoiced frames is calculated by calculating the probability that the present speech frame is unvoiced, calculating the overall probability that any frame will be unvoiced, and calculating the probability that the present speech frame is voiced. The mean of the unvoiced speech frames is then calculated in response to the probability that the present speech frame is unvoiced 15 and the overall probability. In addition, the mean of the voiced speech frame is calculated in response to the probability that the present speech frame is voiced and the overall probability. Advantageously, the calculations of probabilities are performed ~ltili7.ing a maximum likelihood statistical operation.
,~
Advantageously, the generation of the general value is performed utilizing a discriminant analysis procedure, and the speech attributes are speech classifiers.
Advantageously, the decision regions are de_ned by the mean of the 5 unvoiced and voiced speech frames and a weight and threshold value generated in response to the general values of past and present frames and the means of the voiced and unvoiced frames.
The method for detecting the presence of a fun~l~m~nt~l frequency in speech frames compri~es the steps of: generating a general value in response to a 10 set of cl~sifiers ~lefining speech attributes of a present speech frame to indic~t~
the presence of the filn~1~ment~1 frequency, calculating a set of statistical pa~ letel~ in response to the general value, and determining the presence of thefilnd~ment~l frequency in response to the general value and the calculated set of statistical pal~lRt~ . The step of generating the general value is p~,lrolllled 15 utilizing a discrimin~nt analysis procedure. Further, the step of dele ..~ ing the filnfl~mt~nt~l frequency comprises the step of calculating a weight and a threshold value in response to the set of parameters.
Brief Description of the Drawin~
FIG. 1 illusllales, in block diagram form, the present invention; and FIGS. 2 and 3 illustrate, in greater detail, certain functions pelrc~llcd by the voiced detection appa~alus of FIG. 1.
Detailed Description FIG. 1 illustrates an appalalus for pclr IlllPillg the unvoiced/voiced decision operation by _rst l1tili7ing a ~i~crimin~nt voiced detector to process voice 25 classifiers in order to generate a rliscrimin~nt variable or general variable. The latter variable is st~ti~tic~lly analyzed to make the voicing decision. The statistical analysis adapts the threshold utilized in m~king the unvoiced/voiceddecision so as to give reliable pelrollllance in a variety of voice enviro~
Consider now the overall operation of the al~a dlus illustrated in 30 FIG. 1. Cl~csifier gcnclalol 100 is responsive to each frame of voice to generate classifiers which advantageously may be the log of the speech energy, the log ofthe LPC gain, the log area ratio of the first reflection coefficient, and the squared correlation coefficient of two speech segments one frame long which are offset by one pitch period. The calculation of these classifiers involves digitally sampling 35 analog speech, forming frames of the digital samples, and processing those frames - 4 - l 3 3 6 2 0 ~
and is well known in the art. Generator 100 transmits the classifiers to silencedetector 101 and discriminant voiced detector 102 via path 106. Discrimin~nt voiced detector 102 is responsive to the classifiers received via path 106 to calculate the discrimin~nt value, x. Detector 102 pclrOllllS that calculation by5 solving the equation: x = c'y+d. Advantageously, "c" is a vector comprising the weights, "y" is a vector comprising the classifiers, and "d" is a scalar representing a threshold value. Advantageously, the components of vector c are inih~li7~-i asfollows: coll~ol-ent corresponding to log of the speech energy equals 0.3918606,colllpol1ellt cwl~ onding to log of the LPC gain equals -0.0520902, colllponent corresponding to log area ratio of the first reflection coefficient equals 0.5637082, and component corresponding to squared correlation coefficient equals 1.361249;
and d initially equals -8.36454. After calculating the value of the discrimin~ntvariable x, the detector 102 transmits this value via path 111 to statistical calculator 103 and subtracter 107.
Silence detector 101 is responsive to the cl~csifiers tr~nsmittçd via path 106 to determine whether speech is actually present on the data being received on path 109 by classifier generator 100. The intlic~tion of the presence of speech is tr~n~mitted via path 110 to statistical calculator 103 by silence detector 101.
For each frame of speech, detector 102 generates and transmits the discrimin~nt value x via path 111. St~tistic~l calculator 103 m~int~in~ an average of the ~ çrimin~nt values received via path 111 by averaging in the fli.~crimin~nt value for the present, non-silence frame with the ~liscrimin~nt values for previous non-silence frames. St~ti~tic~l calculator 103 is also responsive to the signal 25 received via path 110 to calculate the overall probability that any frame is unvoiced and the probability that any frame is voiced. In addition, st~ti~tic~l calculator 103 calculates the statisdcal value that the discrimin~nt value for the present frame would have if the frame was unvoiced and the statistical value that the discrimin~nt value for the present frame would have if the frame was voiced.30 Advantageously, that st~tistic~l value may be the mean. The calculations p~lrcllllcd by calculator 103 are not only based on the present frame but on previous frames as well. St~ti~ti-~l calculator 103 performs these calculations not only on the basis of the discrimin~nt value received for the present frame via path 106 and the average of the classifiers but also on the basis of a weight and a 35 threshold value defining whether a frame is unvoiced or voiced received via -path 113 from threshold calculator 104.
Calculator 104 is responsive to the probabilities and statistical values of the classifiers for the present frame as generated by calculator 103 and received via path 112 to recalculate the values used as weight value a, and threshold value S b for the present frame. Then, these new values of a and b are tr~nsmitted back to st~tisti-~l calculator 103 via path 113.
Calculator 104 transmits the weight, threshold, and statistical values via path 114 to U/V determin:ltor 105. The latter detector is responsive to the inîo~ ation transmitted via paths 114 and 115 to determine whether or not the 10 frame is unvoiced or voiced and tO LI~lSll~it this decision via path 116.
Consider now in greater detail the operations of blocks 103, 104, 105, and 107 illustrated in FIG. 1. Statistical calculator 103 implements an improvedEM algoliLhm similar to that suggested in the article by N. E. Day entitled ~F.stim~ting the Col~onenls of a Mixture of Normal Distributions", Biometrika, 15 Vol. 56, No. 3, pp. 463-474, 1969. Utilizing the concept of a decaying average, calculator 103 calculates the average for the discrimin~nt values for the present and previous frames by calculating following equations 1, 2, and 3:
n=n+l if n<2000 (1) z = l/n (2) Xn = (1--z) Xn--l + zxn (3) Xn is the discrimin~nt value for the present frame and is received from detector 102 via path 111, and n is the number of frames that have been processed up to 2000. z represents the decaying average coefflcient, and Xn represents the -- 6 - l 3 3 6 2 0 8 average of the discrimin~nt values for the present and past frames. Statistical calculator 103 is responsive to receipt of the z, xn and Xn values to calculate the variance value, T, by first calculating the second moment of Xn, Qn, as follows:
Qn = (l~z)Qn-l + zxn (4) S After Qn has been calculated, T is calculated as follows:
T = Qn--Xn (S) The mean is subtracted from the discrimin~nt value of the present frame as follows:
xn = xn -- Xn (6) 10 Next, calculator 103 determines the probability that the frame represented by the present value xn is unvoiced by solving equation 7 shown below:
P(ulx ) 1 (7) After solving equation 7, c~lcul~tor 103 determines the probability that the discrimin~nt value represents a voiced frame by solving the following:
P(vlxn) = 1--P(ulxn) . (8) -7 l 336208 Next, calculator 103 determines the overall probability that any frame will be unvoiced by solving equation 9 for Pn:
Pn = (1--z) Pn-l + z P(u I xn) . (9) After determining the probability that a frame will be unvoiced, S calculator 103 determines two values, u and v, which give the mean values of discrimin~nt value for both unvoiced and voiced type frames. Value u, statistical average unvoiced value, contains the mean discrimin~nt value if a frarne is unvoiced, and value v, st~ti~ti-~l average voiced value, gives the mean discrimin~nt value if a frame is voiced. Value u for the present frame is solved10 by calculating equation 10, and value v is determined for the present frame by calculating equationlll as follows:
Un = (1--z) Un_l + Z Xn P(ulxn)lpn--ZXn (10) Vn = ( 1--Z) Vn_l + Z Xn P(v I Xn) / ( 1--Pn) --ZXn ( 1 1 ) Calculator 103 now co...n~ ic~tes the u, v, and T values, and probability Pn to 15 threshold calculator 104 via path 112.
Calculator 104 is responsive to this info~ alion to calculate new values for a and b. These new values are then tr~n~mitte~l back to statistical calculator 103 via path 113. This allows rapid adaptations to changing enviro~ e -l~. If n is greater than advantageously 99, values a and b are 20 calculated as follows. Value a is determined by solving the following equa~ion:
a = rl (vn--un) (12) 1--Pn(l--pn) I--1 (un--Vn)2 Value b is determined by solving the following equation:
b =--2 a(un+vn) + log[(l-pn)/pn ] (13) After calculating equations 12 and 13, calculator 104 transmits values a, u, and v to block lOS via path 114.
Determinator 105 is responsive to this tr~n~mitte~ fc.~ ation to decide whether the present frame is voiced or unvoiced. If the value a is positive, then, a frame is declared voiced if the following equation is true:
axn--a(un+vn)/2 > 0; (14) or if the value a is negative, then, a frame is declared voiced if the following10 equation is true:
axn--a(un+vn)/2 < 0 . (15) Equation 14 can also be expressed as:
axn + b--log[(l -Pn) /Pn] >
Equation lS can also be expressed as:
axn + b--log[(l--Pn)/Pn] < --If the previous conditions are not met, determinator 105 declares the frame unvoiced.
In flow chart form, FIGS. 2 and 3 illustrate, in greater detail, the operations performed by the apparatus of FIG. 1. Block 200 implements 5 block 101 of FIG. 1. Blocks 202 through 218 implement statistical calculator 103. Block 222 implements threshold calculator 104, and blocks 226 through 239 implement block 105 of FIG. 1. Subtracter 107 is implemented by both block 208 and block 224. Block 202 calculates the value which represents the average of the discrimin~nt value for the present frame and all previous 10 frames. Block 200 determines whether speech is present in the present frame; and if speech is not present in the present frame, the mean for the discrimin~nt value is subtracted from the present discrimin~nt value by block 224 before control istransferred to decision block 226.
However, if speech is present in the present frame, then the statistical 15 and weight calculations are pelrwmed by blocks 202 through 222. First, the average value is found in block 202. Second, the second moment value is calculated in block 206. The latter value along with the mean value X for the present and past frames is then utilized to calculate the variance, T, also in block 206. The mean X is then subtracted from the discrimin~nt value xn in 20 block 208.
Block 210 calculates the probability that the present frame is unvoiced by utilizing the present weight value a, the present threshold value b, and the discrimin~nt value for the present frame, xn. After calculating the probability that the present frame is unvoiced, the probability that the present frame is voiced is 25 calculated by block 212. Then, the overall probability, Pn, that any frame will be unvoiced is calculated by block 214.
Blocks 216 and 218 calculate two values: u and v. The value u represents the st~tictin~l average value that the discrimin~nt value would have if the frame were unvoiced. Whereas, value v l~ipl~_sents the st~tisti~l average value 30 that the discrimin~nt value would have if the frame were voiced. The actual discrimin~nt values for the present and previous frames are clu~ ,d around either value u or value v. The discr~min~nt values for the previous and present frames are clustered around value u if these frames had been found to be unvoiced;
otherwise, the previous values are clustered around value v. Block 222 then 35 calculates a new weight value a and a new threshold value b. The values a and b - lO- 1 336208 are used in the next sequential frame by the prece~ling blocks in FIG. 2.
Blocks 226 through 239 implement U/V determinator 105 of FIG. 1.
Block 226 determines whether the value a for the present frame is greater than zero. If this condition is true, then decision block 228 is executed. The latterS decision block determines whether the test for voiced or unvoiced is met. If the frame is found to be voiced in decision block 228, then the frame is so marked as voiced by block 230 otherwise the frame is marked as unvoiced by block 232. If the value a is less than zero for the present frame, blocks 234 through 238 are executed and function in a similar manner to blocks 228 through 232.
It is to be understood that the afore-described embodiment is merely illustrative of the principles of the invention and that other arrangements may be devised by those skilled in the art without departing from the spirit and the scope of the invention.
Claims (11)
1. An apparatus for detecting the presence of a fundamental frequency in frames of speech, comprising:
means responsive to a set of classifiers defining speech attributes of one of said frames of speech for generating a general value indicating said presence of said fundamental frequency;
means responsive to said general value for calculating a set of statistical parameters;
means for calculating a threshold value in response to said set of said parameters;
means for calculating a weight value in response to said set of said parameters;
means for communicating said weight value and said threshold value to said means for calculating said set of parameters to be used for calculating another set of parameters for another one of said frames of speech; and means responsive to said weight value and said threshold value and the calculated set of statistical parameters for determining said presence of said fundamental frequency in said present one of said frames of speech.
means responsive to a set of classifiers defining speech attributes of one of said frames of speech for generating a general value indicating said presence of said fundamental frequency;
means responsive to said general value for calculating a set of statistical parameters;
means for calculating a threshold value in response to said set of said parameters;
means for calculating a weight value in response to said set of said parameters;
means for communicating said weight value and said threshold value to said means for calculating said set of parameters to be used for calculating another set of parameters for another one of said frames of speech; and means responsive to said weight value and said threshold value and the calculated set of statistical parameters for determining said presence of said fundamental frequency in said present one of said frames of speech.
2. The apparatus of claim 1 wherein said generating means comprises means for performing a discriminant analysis to generate said general value.
3. The apparatus of claim 2 wherein said means for calculating said set of parameters further responsive to the communicated weight value and threshold value and another general value of said other one of said frames for calculating another set of statistical parameters.
4. The apparatus of claim 3 wherein said means for calculating said set of parameters further comprises means for calculating the average of said general values over said present and previous ones of said speech frames; and means responsive to said average of said general values for said present and previous ones of said speech frames and said communicated weight value and threshold value and said other general value for determining said other set of statistical parameters.
5. An apparatus for detecting the presence of a fundamental frequency in frames of non-training set speech, comprising:
means responsive to a set of classifiers defining speech attributes of each of a present and past ones said frames of non-training set speech for generating a general value indicating said presence of said fundamental frequency;
means for calculating the variance of said general values over said present and previous ones of said speech frames;
means responsive to present and past ones of said frames for calculating the probability that said present one of said frames is unvoiced;
means responsive to said present and past ones of said frames and said probability that said present one of said frames is unvoiced for calculating theoverall probability that any frame will be unvoiced;
means for calculating the probability that said present one of said frames is voiced;
means responsive to said probability that said present one of said frames is unvoiced and said overall probability and said variance for calculating a mean of said unvoiced ones of said frames;
means responsive to said probability that said present one of said frames is voiced and said overall probability and said variance for calculating a mean of said voiced ones of said frames;
means responsive to said mean for unvoiced ones of said frames and said mean of voiced ones of said frames and said variance for determining decision regions; and means for making the determination of said presence of said fundamental frequency in response to said decision regions for said present one of said frames.
means responsive to a set of classifiers defining speech attributes of each of a present and past ones said frames of non-training set speech for generating a general value indicating said presence of said fundamental frequency;
means for calculating the variance of said general values over said present and previous ones of said speech frames;
means responsive to present and past ones of said frames for calculating the probability that said present one of said frames is unvoiced;
means responsive to said present and past ones of said frames and said probability that said present one of said frames is unvoiced for calculating theoverall probability that any frame will be unvoiced;
means for calculating the probability that said present one of said frames is voiced;
means responsive to said probability that said present one of said frames is unvoiced and said overall probability and said variance for calculating a mean of said unvoiced ones of said frames;
means responsive to said probability that said present one of said frames is voiced and said overall probability and said variance for calculating a mean of said voiced ones of said frames;
means responsive to said mean for unvoiced ones of said frames and said mean of voiced ones of said frames and said variance for determining decision regions; and means for making the determination of said presence of said fundamental frequency in response to said decision regions for said present one of said frames.
6. The apparatus of claim 5 wherein said means for calculating said probability that said present one of said frames is unvoiced performed a maximumlikelihood statistical operation.
7. The apparatus of claim 6 wherein said means for calculating said probability that said present one of said frames is unvoiced further responsive to a weight value and threshold value to perform said maximum likelihood statistical operation.
8. A method for detecting the presence of a fundamental frequency in frames of speech comprising the steps of:
generating a general value in response to a set of classifiers defining speech attributes of one of said frames of speech to indicate said presence of said fundamental frequency;
calculating a set of statistical parameters in response to said general value; and determining said presence of said fundamental frequency in said one of said frames;
said step of determining comprises the steps of calculating a threshold value in response to said set of said parameters;
calculating a weight value in response to said set of said parameters;
and communicating said weight value and said threshold value to said means for calculating said set of parameters to be used for calculating another set of parameters for another one of said frames of speech.
generating a general value in response to a set of classifiers defining speech attributes of one of said frames of speech to indicate said presence of said fundamental frequency;
calculating a set of statistical parameters in response to said general value; and determining said presence of said fundamental frequency in said one of said frames;
said step of determining comprises the steps of calculating a threshold value in response to said set of said parameters;
calculating a weight value in response to said set of said parameters;
and communicating said weight value and said threshold value to said means for calculating said set of parameters to be used for calculating another set of parameters for another one of said frames of speech.
9. The method of claim 8 wherein said step of generating comprises the step of performing a discriminant analysis to generate said general value.
10. The method of claim 9 wherein said step of calculating said set of parameters further responsive to the communicated weight and threshold value andanother general value of said other one of said frames for calculating another set of statistical parameters.
11. The method of claim 10 wherein said step of calculating said set of parameters further comprises the steps of calculating the average of said general values over said present and previous ones of said speech frames; and determining said other set of statistical parameters in response to said average of said general values for said present and previous ones of said speechframes and said communicated weight and threshold value and said other general values.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US3429887A | 1987-04-03 | 1987-04-03 | |
| US034,298 | 1987-04-03 |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CA1336208C true CA1336208C (en) | 1995-07-04 |
Family
ID=21875533
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CA000562765A Expired - Fee Related CA1336208C (en) | 1987-04-03 | 1988-03-29 | Adaptive threshold voiced detector |
Country Status (9)
| Country | Link |
|---|---|
| EP (1) | EP0309561B1 (en) |
| JP (1) | JPH0795239B2 (en) |
| AT (1) | ATE83329T1 (en) |
| AU (1) | AU598933B2 (en) |
| CA (1) | CA1336208C (en) |
| DE (1) | DE3876569T2 (en) |
| HK (1) | HK21794A (en) |
| SG (1) | SG60993G (en) |
| WO (1) | WO1988007739A1 (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| DE3875894T2 (en) * | 1987-04-03 | 1993-05-19 | American Telephone & Telegraph | ADAPTIVE MULTIVARIABLE ANALYSIS DEVICE. |
| US5195138A (en) * | 1990-01-18 | 1993-03-16 | Matsushita Electric Industrial Co., Ltd. | Voice signal processing device |
| US5204906A (en) * | 1990-02-13 | 1993-04-20 | Matsushita Electric Industrial Co., Ltd. | Voice signal processing device |
| EP0459384B1 (en) * | 1990-05-28 | 1998-12-30 | Matsushita Electric Industrial Co., Ltd. | Speech signal processing apparatus for cutting out a speech signal from a noisy speech signal |
Family Cites Families (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPS60114900A (en) * | 1983-11-25 | 1985-06-21 | 松下電器産業株式会社 | Voice/voiceless discrimination |
| JPS60200300A (en) * | 1984-03-23 | 1985-10-09 | 松下電器産業株式会社 | Voice head/end detector |
| JPS6148898A (en) * | 1984-08-16 | 1986-03-10 | 松下電器産業株式会社 | Voice/voiceless discriminator for voice |
| DE3875894T2 (en) * | 1987-04-03 | 1993-05-19 | American Telephone & Telegraph | ADAPTIVE MULTIVARIABLE ANALYSIS DEVICE. |
-
1988
- 1988-01-12 WO PCT/US1988/000031 patent/WO1988007739A1/en active IP Right Grant
- 1988-01-12 AT AT88903995T patent/ATE83329T1/en not_active IP Right Cessation
- 1988-01-12 DE DE8888903995T patent/DE3876569T2/en not_active Expired - Fee Related
- 1988-01-12 AU AU17007/88A patent/AU598933B2/en not_active Ceased
- 1988-01-12 JP JP63503536A patent/JPH0795239B2/en not_active Expired - Fee Related
- 1988-01-12 EP EP88903995A patent/EP0309561B1/en not_active Expired - Lifetime
- 1988-03-29 CA CA000562765A patent/CA1336208C/en not_active Expired - Fee Related
-
1993
- 1993-05-07 SG SG609/93A patent/SG60993G/en unknown
-
1994
- 1994-03-10 HK HK217/94A patent/HK21794A/en not_active IP Right Cessation
Also Published As
| Publication number | Publication date |
|---|---|
| ATE83329T1 (en) | 1992-12-15 |
| AU598933B2 (en) | 1990-07-05 |
| JPH0795239B2 (en) | 1995-10-11 |
| DE3876569T2 (en) | 1993-04-08 |
| EP0309561A1 (en) | 1989-04-05 |
| DE3876569D1 (en) | 1993-01-21 |
| HK21794A (en) | 1994-03-18 |
| EP0309561B1 (en) | 1992-12-09 |
| SG60993G (en) | 1993-07-09 |
| WO1988007739A1 (en) | 1988-10-06 |
| JPH01502858A (en) | 1989-09-28 |
| AU1700788A (en) | 1988-11-02 |
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