Source: http://www.google.com/patents/US5103481?ie=ISO-8859-1
Timestamp: 2014-03-13 10:43:05
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Matched Legal Cases: ['art 3', 'art 1', 'art 2', 'art 11', 'art 12', 'art 13', 'art 15', 'art 16', 'art 15', 'art 16', 'art 12', 'art 13', 'art 12', 'art 22', 'art 23', 'art 24', 'art 22', 'art 24', 'art 22', 'art 23', 'art 24', 'art 24', 'art 36']

Patent US5103481 - Voice detection apparatus - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsSpeech presence versus silence is decided by a discriminator which can use a certain combination of parameter values: signal power, prediction error power, prediction error power deviation, and zero crossings....http://www.google.com/patents/US5103481?utm_source=gb-gplus-sharePatent US5103481 - Voice detection apparatusAdvanced Patent SearchPublication numberUS5103481 APublication typeGrantApplication numberUS 07/507,658Publication dateApr 7, 1992Filing dateApr 10, 1990Priority dateApr 10, 1989Fee statusPaidAlso published asCA2014132A1, CA2014132C, DE69028428D1, DE69028428T2, EP0392412A2, EP0392412A3, EP0392412B1Publication number07507658, 507658, US 5103481 A, US 5103481A, US-A-5103481, US5103481 A, US5103481AInventorsKenichi Abiru, Kohei Iseda, Yoshihiro Tomita, Shigeyuki UnagamiOriginal AssigneeFujitsu LimitedExport CitationBiBTeX, EndNote, RefManPatent Citations (6), Referenced by (10), Classifications (9), Legal Events (5) External Links: USPTO, USPTO Assignment, EspacenetVoice detection apparatusUS 5103481 AAbstract Speech presence versus silence is decided by a discriminator which can use a certain combination of parameter values: signal power, prediction error power, prediction error power deviation, and zero crossings.
What is claimed is: 1. A voice detection apparatus comprising:signal power calculation means for receiving an input voice signal that comprises a plurality of frames and has voiced and silent intervals and for calculating a signal power of the input voice signal for each of the frames; zero crossing counting means for counting a number of polarity inversions of the input voice signal for each of the frames; adaptive prediction filter means for obtaining a prediction error signal of the input voice signal for each of the frames; error signal power calculation means for calculating an error signal power of the prediction error signal for each of the frames; power comparing means for comparing the signal power of the input voice signal and the error signal power of the prediction error signal and for obtaining a power ratio responsive to the comparing; and discriminating means for discriminating the voiced and silent intervals based on the signal power, the counted number of polarity inversions and the power ratio, said discriminating means including:first means for discriminating the voiced and silent intervals of the input voice signal based on the counted number of polarity inversions, and second means for determining an absolute value of a difference of the power ratios between the frames, and for discriminating whether a frame is a voiced interval or a silent interval depending on a comparison of the absolute value with a first threshold value and whether a previous frame is a voiced interval or a silent interval when the signal power of the input voice signal is less than a second threshold value. 2. The voice detection apparatus as claimed in claim 1, further comprising:means for sampling the input voice signal, and wherein said signal power calculation means includes means for calculating the signal power of the input voice signal based on ##EQU2## where SP denotes the signal power, n denotes a number of the samples, X.sub.i denotes sectioning the input voice signal at predetermined time intervals and N denotes a number of the frames obtained from the sectioning of the input voice signal at the predetermined time intervals. 3. The voice detection apparatus as claimed in claim 1, wherein said error signal power calculation means includes means for calculating the signal power of the prediction error signal.
10. The voice detection apparatus as claimed in claim 8, wherein said prediction gain deviation calculation means includes:adaptive predictor means for calculating a prediction error for each of the frames. 11. The voice detection apparatus as claimed in claim 10, wherein said prediction gain deviation calculation means includes means for calculating the prediction gain based on G=-10log.sub.10 [ΣE.sup.2 /P], where G denotes the prediction gain, P denotes the signal power and E denotes the prediction error.
BACKGROUND OF THE INVENTION The present invention generally relates to voice detection apparatuses, and more particularly to a voice detection apparatus for detecting voiced and silent intervals of a voice signal.
FIG. 2 is a flow chart for explaining the operation of the discriminating part 3 of the voice detection apparatus. A step S0 discriminates whether or not a voice signal power SP calculated in the signal power calculation part 1 is greater than a threshold value SP.sub.th. When the discrimination result in the step S0 is YES, a voiced interval is detected and a step S1 sets the threshold value SP.sub.th to SP.sub.th =SP.sub.th2 and the process returns to the step S0. On the other hand, when the discrimination result in the step S0 is NO, a step S2 compares a zero crossing number ZC which is counted in the zero crossing counting part 2 with threshold values ZC.sub.v and ZC.sub.f.
FIG. 3 shows a relationship of the threshold values ZC.sub.v and ZC.sub.f, the voiced interval (voiced and voiceless sounds) and the silent interval (noise). It is known that the silent interval occurs only when ZC.sub.v &lt;ZC&lt;ZC.sub.f. Accordingly, when ZC&gt;ZC.sub.f and ZC&lt;ZC.sub.v and the voiced interval is detected in the step S2, the process returns to the step S0 via the step S1. However, when ZC.sub.f &gt;ZC&gt;ZC.sub.v and the silent interval is detected in the step S2, a step S3 sets the threshold value SP.sub.th to SP.sub.th =SP.sub.th1 and the process returns to the step S0.
FIG. 4 shows a relationship of the threshold values SP.sub.th1 and SP.sub.th2. A hysteresis characteristic is given to the threshold values at the times when the voiced and silent intervals are detected, and the threshold value is set to SP.sub.th1 for the transition from the silent interval to the voiced interval and the threshold value is set to SP.sub.th2 for the transition from the voiced interval to the silent interval, so that no chattering is generated in the detection result.
SUMMARY OF THE INVENTION Accordingly, it is a general object of the present invention to provide a novel and useful voice detection apparatus in which the problems described above are eliminated.
DESCRIPTION OF THE PREFERRED EMBODIMENTS A description will be given of an operating principle of a first embodiment of a voice detection apparatus according to the present invention, by referring to FIG. 5. The voice detection apparatus shown in FIG. 5 comprises a signal power calculation part 11, a zero crossing counting part 12, a discriminating part 13, an adaptive prediction filter 14, an error signal power calculation part 15 and a power comparing part 16. The adaptive prediction filter 14 obtains a prediction error signal of an input voice signal. The error signal power calculation part 15 obtains the power of the prediction error signal. The power comparing part 16 obtains a power ratio of the input voice signal power and the prediction error signal power. In addition to the discrimination of the voiced/silent interval based on a zero crossing number which is obtained in the zero crossing counting part 12, the discriminating part 13 compares an absolute value of a difference of the power ratios between frames with a threshold value and also discriminates the voiced/silent state of a present frame depending on whether a previous frame is voiced or silent when the input voice signal power is smaller than a threshold value.
In FIG. 6, an input voice signal power SP is given by the following formula based on an input voice signal x.sub.i. ##EQU1## In the above formula, n denotes a number of samples, and N denotes a number of frames which is obtained by sectioning the input voice signal x.sub.i at predetermined time intervals.
In FIG. 7, the zero crossing counting part 12 comprises a highpass filter 21, a polarity detection part 22, a 1-sample delay part 23, a polarity inversion detection part 24 and a counter 25. The input voice signal x.sub.i is supplied to the highpass filter 21 to eliminate a D.C. offset. The polarity detection part 22 detects the polarity of the input voice signal x.sub.i. The polarity inversion detection part 24 receives the input voice signal x.sub.i from the polarity detection part 22 and a delayed input voice signal x.sub.i which is delayed by one sample in the 1-sample delay part 23. The polarity inversion detection part 24 detects the polarity inversion based on a present sample and a previous sample of the input voice signal x.sub.i. The counter 25 counts the number of polarity inversions detected by the polarity inversion detection part 24. The counter 25 is reset for every frame in response to a reset signal RST.
G=10log.sub.10 (SP/EP)
In this case, the absolute value of G.sub.t -G.sub.t-1 is calculated because the power may change from a large value to a small value or vice versa between the frames.
The step S10 discriminates whether or not the difference GD of the prediction gains F between the frames is greater than a preset threshold value GD.sub.th. When the discrimination result in the step S10 is YES, a step S11 discriminates whether or not the previous frame is a voiced interval by referring to the voiced/silent discrimination information which is stored in the previous frame. When the discrimination result in the step S11 is YES, it is discriminated that the previous frame is silent and a step S12 sets the silent flag SF to "1". On the other hand, when the discrimination result in the step S11 is NO, it is discriminated that the previous frame is a voiced interval and a step S13 sets the voiced flag VF to "1".
The discrimination result is stored in the voiced and silent flags VF and SF in the above described manner in the steps S4, S5, S12, S13, S15 and S16. When the voiced flag VF is set to "1", the discrimination result in the step S17 is YES and the voiced interval is detected. In this case, the threshold value SP.sub.th of the signal power SP is renewed in the step S1. On the other hand, when no voiced flag is set to "1", the discrimination result in the step S17 is NO and the silent interval is detected. In this case, the threshold value SP.sub.th of the signal power SP is renewed in the step S3.
G=-10log.sub.10 [&#931;E.sup.2 /P]
FIG. 12 shows an operation of the discriminating part 36 for discriminating the voiced/silent interval. When a discriminating operation is started in a step S21, a step S22 discriminates whether or not the signal power P of the input voice frame is greater than or equal to a predetermined threshold value P.sub.th. When the discrimination result in the step S22 is YES, a step S24 detects that the input voice frame is voiced.
On the other hand, when the discrimination result in the step S22 is NO, a step S23 discriminates whether or not the zero crossing number Z is greater than or equal to a threshold value Z.sub.th1 and is less than or equal to a threshold value Z.sub.th2, so as to make a further discrimination on whether the input voice frame is voiced or silent. Generally, the voice signal has a low-frequency component and a high-frequency component in the voiced interval, and the voiced interval does not include much intermediate frequency component. On the other hand, noise includes all frequency components. For this reason, when the discrimination result in the step S23 is NO, the step S24 detects that the input voice frame is voiced.
When the discrimination result in the step S23 is YES, a step S25 discriminates whether or not the prediction gain deviation D is greater than or equal to a threshold value D.sub.th, to as to make a further discrimination on whether the input voice frame is voiced or silent. Generally, the prediction gain G has a large value when the input voice frame is voiced and a small value when the input voice frame is silent such as the case of the noise. Accordingly, in a case where the previous frame is voiced and the prevent frame is silent or in a case where the previous frame is silent and the present frame is voiced, the prediction gain deviation D has a large value.
But when discriminating the voiced/silent interval based on the prediction gain deviation D and when the level of the background noise is large, the prediction gain deviation D between the present frame and the previous frame is small even when there is a transition from the voiced state to the silent state or vice versa. Accordingly, when the prediction gain deviation D is less than or equal to the threshold value D.sub.th under such conditions, the step S29 regards the voiced/silent state of the previous frame as the voiced/silent state of the present frame even when the state changes from the voiced state to the silent state or vice versa between the previous and present frames.
The prediction gain detection means 41 detects a prediction gain G of the present frame. The prediction gain deviation detection means 42 detects a prediction gain deviation D between the present frame and the previous frame. The discrimination means 43 discriminates whether the present frame is a voiced interval or a silent interval based on a comparison of the prediction gain G with a threshold value G.sub.th and a comparison of the prediction gain deviation G with a threshold value D.sub.th.
For example, the discrimination means 43 first discriminates the voiced/silent state based on whether or not the prediction gain deviation D is greater than or equal to the threshold value D.sub.th, and when the discrimination result is the silent state, the discrimination result is corrected by discriminating the voiced/silent state based on whether or not the prediction gain G is greater than or equal to the threshold value G.sub.th. As an alternative, the discrimination means 43 first discriminates the voiced/silent state based on whether or not the prediction gain G is greater than or equal to the threshold value G.sub.th, and when the discrimination result is the voiced state, the discrimination result is corrected by discriminating the voiced/silent state based on whether or not the prediction gain deviation D is greater than or equal to the threshold value D.sub.th.
When a discriminating operation is started in a step S41 shown in FIG. 14A, a step S42 discriminates whether or not the signal power P of the input voice frame is greater than or equal to a predetermined threshold value P.sub.th. When the discrimination result in the step S42 is YES, a step S43 detects that the input voice frame is voiced.
On the other hand, when the discrimination result in the step S42 is NO, a step S44 discriminates whether or not the zero crossing number Z is greater than or equal to a threshold value Z.sub.th so as to make a further discrimination on whether the input voice frame is voiced or silent. When the discrimination result in the step S44 is YES, a step S45 detects that the input voice frame is a pseudo voiced interval.
FIG. 14B shows the step S45. A step S61 discriminates whether or not the signal power P of the input voice signal is greater than or equal to a threshold value P.sub.th*. When the discrimination result in the step S61 is NO, a step S62 detects the silent interval. On the other hand, when the discrimination result in the step S61 is YES, a step S63 detects the voiced interval The threshold value P.sub.th* is used to forcibly discriminate the silent interval when the signal power P is in the order of the idle channel noise and small, even when the input voice frame is once discriminated as the voiced interval. Hence, this threshold value P.sub.th* is set to an extremely small value so that the silent state of the input voice frame can absolutely be discriminated.
When the discrimination result in the step S44 is NO, a step S46 discriminates whether or not the prediction gain deviation D is greater than or equal to a threshold value D.sub.th, to as to make a further discrimination on whether the input voice frame is voiced or silent. When the discrimination result in the step S46 is YES, it is detected that a transition occurred between the voiced and silent intervals. A step S47 obtains a state which is inverted with respect to the state of the previous frame. In other words, a voiced state is obtained when the previous frame is silent and a silent state is obtained when the previous frame is voiced. When the previous frame is silent, a step S48 detects that the input voice frame is pseudo voiced and the process shown in FIG. 14B is carried out. On the other hand, when the previous frame is voiced, a step S49 detects that the input voice frame is silent.
When the discrimination result in the step S46 is NO, a step S50 discriminates whether or not an absolute value of the prediction gain G is greater than or equal to zero and is less than or equal to a threshold value G.sub.th. As described above, when the background noise is large, the prediction gain deviation D may be smaller than the threshold value D.sub.th even when there is a transition from the voiced state to the silent state or vice versa. However, the absolute value of the prediction gain G itself has a large value for the voiced signal and a small value for the noise. For this reason, a step S52 detects the silent interval when the discrimination result in the step S50 is YES. On the other hand, when the discrimination result in the step S50 is NO, a step S51 obtains a state which is the same as the state of the previous frame. In other words, a voiced state is obtained when the previous frame is voiced and a silent state is obtained when the previous frame is silent. When the previous frame is voiced, the step S48 detects that the input voice frame is pseudo voiced. On the other hand, when the previous frame is silent, the step S49 detects that the input voice frame is silent.
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