Source: http://www.google.com/patents/US7720679?dq=6519629
Timestamp: 2015-05-04 21:51:24
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Matched Legal Cases: ['art 20', 'art 40', 'art 22', 'art 230', 'art 240', 'art 240', 'art 240', 'art 250', 'art 250', 'art 230', 'art 240', 'art 250', 'art 260', 'art 210', 'art 220', 'art 230', 'art 240', 'art 250', 'art 260', 'art 260', 'art 260', 'art 260']

Patent US7720679 - Speech recognition apparatus, speech recognition apparatus and program thereof - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign inAdvanced Patent SearchPatentsProvided is a method for canceling background noise of a sound source other than a target direction sound source in order to realize highly accurate speech recognition, and a system using the same. In terms of directional characteristics of a microphone array, due to a capability of approximating a power...http://www.google.com/patents/US7720679?utm_source=gb-gplus-sharePatent US7720679 - Speech recognition apparatus, speech recognition apparatus and program thereofAdvanced Patent SearchPublication numberUS7720679 B2Publication typeGrantApplication numberUS 12/236,588Publication dateMay 18, 2010Filing dateSep 24, 2008Priority dateMar 14, 2002Fee statusPaidAlso published asUS7478041, US20030177006, US20090076815Publication number12236588, 236588, US 7720679 B2, US 7720679B2, US-B2-7720679, US7720679 B2, US7720679B2InventorsOsamu Ichikawa, Tetsuya Takiguchi, Masafumi NishimuraOriginal AssigneeNuance Communications, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (25), Non-Patent Citations (16), Referenced by (14), Classifications (18), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetSpeech recognition apparatus, speech recognition apparatus and program thereof
US 7720679 B2Abstract
This is a method for subtracting �a signal in which a noise component is a main component� from the output by the delay and sum. When there are two microphones, the signal thereof is generated as follows. First, the phases of the one of a combination of signals set in-phase with respect to the target sound source is inversed to be added up with the other, whereby a target voice component is canceled. Then, in the noise section, an adaptive filter is designed so as to minimize noise.
Nunoda, Nagata, and Abe: �Voice recognition under unsteady noise using two-channel voice detection�, technical research report 2001-25 by Institute of Electronics, Information and Communication Engineers [Nonpatent Document 2]
Mizumachi and Akagi: pp. 503-512, �Noise cancellation method by spectral subtraction using microphone pair�, treatise A Vol. J82-A No. 4, 1999 by Institute of Electronics, Information and Communication Engineers� [Nonpatent Document 3]
Asano, Hayami, Yamada, and Nakamura: �Application of voice emphasis method using sub-spacing method to voice recognition�, technical research report EA97-17 by Institute of Electronics, Information and Communication Engineers� [Nonpatent Document 4]
Nagata, and Abe: pp. 503-512, �Studies on speaker tracking 2-channel microphone array�, treatise A Vol. J82-A No. 4 by Institute of Electronics, Information and Communication engineers� As described above, in the speech recognition technology, when realizing speech recognition with high accuracy in an environment of a distance between the microphone and the speaker, cancellation of background noise becomes an important task. The method for assuming the sound source direction by using the microphone array to cancel noise is considered as one of the most effective means.
In addition, the method for using the delay and sum in combination with the 2-channel spectral subtraction, since the noise component is estimated for the cancellation, can suppress the background noise to a certain extent. However, since the noise is estimated by �a point,� an accuracy of the estimation has not always been high.
X + [Equation 1]
Here, denotes a weight coefficient of directional sound source profile of a target direction, and a weight coefficient of nondirectional background sound profile. These coefficients are decided so as to minimize an evaluation function represented by the following equation 2.
and for giving the minimum value are obtained by the following equation 3.
However, and must be assured.
After the coefficients have been obtained, a power of only a target sound source including no noise components can be obtained. A power at its frequency is given as In addition, in an environment of recording a voice, not only background noise of a noise source, but also predetermined noise (directional noise) from a specific direction can be assumed. If its coming direction can be assumed, directional sound source profile for the directional noise is obtained from the profile database 50 to be added as a resolution element of a right side of the equation 1.
As a result, the profile fitting unit 33 assumes a voice power of each frequency of only a target sound source including no noise components to be The assumed voice power of each frequency is transferred to the spectrum reconstruction unit 34.
The spectrum reconstruction unit 34 collects the voice powers of all the frequency bands assumed by the profile fitting unit 33 to structure voice data of a noise component-suppressed frequency domain. If smoothing is carried out at the profile fitting unit 33, at the spectrum reconstruction unit 34, inverse-smoothing for construction as a inverse-filter of smoothing may be carried out to sharpen time fluctuation. Assuming that is a inverse smoothing output (power spectrum), in order to suppress excessive fluctuation in inverse smoothing, a limiter may be incorporated to limit fluctuation to and For this limiter, two types of processes, i.e., a sequential process executing a limit at each state of the inverse filter, and a post process executing a limit after the end of inverse-filtering, are conceivable. From experience, preferably, is set for the sequential process, and for the post process.
The delay and sum unit 31 represents a delay amount by sampling points. This delay amount is multiplied by a sampling frequency to become actual delay time. Assuming that a minute width of a delay amount to be changed is sample, and the delay amount is changed to an M steps in each of positive and negative directions, a maximum delay amount becomes M [multiplied by] sample, and a minimum delay amount becomes −M [multiplied by] sample. In this case, a delay and sum output of an m-th stage becomes a value represented by the following equation 4.
x(m,t)= s(n,t−(n−1) [Equation 4]
The Fourier transformation unit 32 cuts up the voice data x(m, t) of the timed domain for each short-time voice frame interval to be converted into voice data of a frequency domain by Fourier transformation. Further, the voice data of the frequency domain is converted into a power distribution (m) for each frequency band. Here, a suffix denotes a representative frequency of each frequency band. The suffix i denotes a number of a voice frame. If a voice frame interval represented by sampling points is frame_size, there is a relation of t=i [multiplied by] frame_size.
The observed profile (m) is transferred to the profile fitting unit 33. However, if time-direction smoothing is carried out as a preprocess at the profile fitting unit 33, the observed profile is to be a value represented by the following equation 5, where profile before smoothing is (m), and a filter width is W, and a filter coefficient is Cj.
(m)= c j (m), here, cj=1 [Equation 5]
For this process, the observed profile X (m) received from the Fourier transformation unit 32, sound source location information m0 assumed by the sound source localization part 20, given directional sound source profile P (m0, m) for a sound source from a direction represented by a direction m, and given profile Q (m) for a nondirectional background sound are inputted to the profile fitting unit 33. Here, similarly to the observed profile, for the given profile, a direction parameter m is set by a sampling point unit of one-side by M steps.
A weight coefficient of the directional sound source profile of the target direction, and a coefficient of the nondirectional background sound profile are obtained by the following equation 6. In the equation, suffixes and i are omitted. The process is executed for each frequency band and each voice frame i.
a 0 = {Q(m)}2 a i = {P(m)}2 a 2 = {P(m) (m)}
a 3 = {X(m) (m)} a 4 = {X(m) (m)}
However, since and should not be negative values, the following is assumed:
If =a4/a0 If =0, =a3/a1 Then, spectrum reconstruction is carried out by the spectrum reconstruction unit 34 (step 605).
The spectrum reconstruction unit 34 obtains voice output data Z of a noise-suppressed frequency domain based on a result of decomposition by the profile fitting unit 33 in the following manner.
First, if no smoothing is executed at the profile fitting unit 33, there is a relation of Z =Y directly.
Here, Y = (m0, m0)
On the other hand, if smoothing is executed at the profile fitting unit 33, inverse smoothing accompanying a fluctuation limit represented by the following equation 7 is executed to obtain Z .
This voice output data Z is outputted as a processing result to the speech recognition part 40 (step 606).
The delay and sum unit 36 receives voice data in a frequency domain, and delays the voice data by a given predetermined phase delay amount to add them up. In FIG. 7, a plurality of delay and sum units is described for respective preset phase delay amounts (minimum phase delay amount, . . . , − 0, + . . . , maximum phase delay amount). For example, if distances between the microphones in the microphone array 111 are constant and a phase delay amount is + a phase of voice data recorded by an n-th microphone is delayed by (n−1) [multiplied by] . Then, a number N of voice data is similarly delayed to be added up. This process is executed for each of preset phase delay amounts from the minimum delay amount to the maximum delay amount. This phase delay amount corresponds to a direction of directional characteristics of the microphone array 111. Therefore, similarly to the case of the configuration shown in FIG. 3, an output of the delay and sum unit 36 comes to be voice data at each stage when directional characteristics of the microphone array 111 are changed stepwise from a minimum angle to a maximum angle.
The profile fitting unit 23 averages voice power distributions transferred from the Fourier transformation part 22 within a short time to generate a profile observation value for each frequency. Then, the obtained observation value is approximately executed a decomposition to given profile. In this case, as directional sound source profile all directional sound source profiles stored in the profile database 50 are sequentially selected to be applied and, by the above-described method mainly based on the equation 2, coefficients and are obtained. After the coefficients and are obtained, a residual of an evaluation function can be obtained by substitution of the coefficients into the equation 2. The obtained residual of the evaluation function for each frequency band is transferred to the residual evaluation unit 24.
The residual evaluation unit 24 sums up the residuals of the evaluation function of the respective frequency bands w received from the profile fitting unit 23. In this case, in order to enhance accuracy of the sound source localization, the residuals may be summed up incorporating weight in a high frequency band. Given directional sound source profile selected at the time when the total residual becomes minimum represents an assumed sound source location. That is, a sound source location at the time when the given directional sound source profile is determined is a sound source location to be assumed here.
The profile fitting unit 23 first selects, as given directional sound source profile used for decomposition, different profile sequentially from the given directional sound source profiles stored in the profile database 50 (step 904). Specifically, the operation corresponds to changing of m0 of the given directional sound source profile P (m0, m) for a sound source from a direction m0. Then, decomposition is executed for the selected given directional sound source profile (steps 905, and 906).
In the decomposition process by the profile fitting unit 23, by a process similar to the decomposition (step 604) described above with reference to FIG. 6, a weight coefficient of directional sound source profile of a target direction, and a weight coefficient of nondirectional background sound profile are obtained. Then, by using the obtained coefficients and of the directional sound source profile of the target direction and the nondirectional background sound profile, a residual of an evaluation function is obtained by the following equation 8 (step 907).
= {X (m)− (m 0 ,m)− (m)}2 [Equation 8]
ALL = C( [Equation 9]
Here, C( denotes a weight coefficient, and simply can be all 1.
Then, given directional sound source profile for minimizing is selected, and outputted as location information (step 909).
In order to prepare for inevitable observation errors when a predetermined target is observed, the observation target is modeled in a certain form to execute maximum likelihood estimation. According to the embodiment, by using the property that �spectrum envelope is changed continuously� as a voice model of the observation target, a smoothing solution of a spectrum frequency direction is defined.
T)= T)+Y( T) @ @ @ @ (hereinafter, S is also described as ). [Equation 10]
Here, denotes a smoothing solution averaging powers S of a target voice included in the main beam former among adjacent sub-band points. Y denotes an error from the smoothing solution, which is called a modeling error. Also, denotes a frequency, and T a time-sequential number of a voice frame.
Z( T)=S( T)+V( T) [Equation 11]
Here, V denotes an observation error. This observation error is large at a frequency where aliasing occurs. After an observation error Z is obtained, a conditional probability distribution P( ) at a power S of a target voice is represented by the following equation 12 based on Bayes' formula.
=P(Z ) )/P(Z) [Equation 12]
In this case, an assumption value by a model is used if the observation error V is large, and the observation value Z itself is used if the observation error V is small, whereby reasonable assumption is made.
T)= T)+(p( T)/r( T)) T)− T)} [Equation 13]
(hereinafter, is also described as )
p( T)=(q( T)−1 +r( T)−1)−1 [Equation 14]
q( T)=E[{Y( T j)}2] T [Equation 15]
r( T)=E[{V( ,T j)}2] T [Equation 16]
Here, q denotes variance of a modeling error Y, and r variance of an observation error V. In the equations 15, 16, average values of Y, V are assumed to be 0. Here, as shown in FIG. 11 showing a range of variance measurement, E[ ] T represents an operation of taking an expected value of m [multiplied by] n points around @ T. The letters and Tj represent point in m [multiplied by] n points.
Z ( T)= T)− V ( T) T) [Equation 17]
For the observation error variance r, first, a stationary nature is assumed to set r( ). As a power S of a target voice is 0 in the noise section, by observing the observation value Z, the above can be obtained from the equations 11, and 16. In this case, a range of an operation of measuring variance becomes similar to a range (a) of FIG. 11.
f( T)=E[{Z( T j)− Z ( T j)}2] T [Equation 18]
E[{Y( ,Tj)+V( ,Tj)}2] T E[{Y( ,T j)}2] T +E[{V( T j)}2] T =q( T)+r Here, it is assumed that there is no correlation between the modeling error Y and the observation error V. As the observation error variance r has been obtained, by observing f in the voice section, modeling error variance q can be obtained from the equation 18. In this case, a range of an operation of measuring variance is similar to a range (b) shown in FIG. 11.
As shown in FIG. 12, after obtaining a power spectrum Z( T) after noise suppression of a voice frame T from the noise suppression part 230 (step 1201), the variance measurement part 240 determines whether the voice frame T belongs to the voice section or to the noise section (step 1202). Determination for the voice frame T can be made by using a conventionally known method.
If the inputted voice frame T belongs to the noise section, the variance measurement part 240 refers the observation error variance r( ) to past history to execute recalculation (updating) according to the equations 11, 16 (step 1203).
On the other hand, if the inputted voice frame T belongs to the voice section, the variance measurement part 240 first makes a smoothing solution S−( ,T) from the power spectrum Z( T) as the observation value by the equation 17 (step 1204). Then, by the equation 18, the modeling error variance q( ,T) is recalculated (updated). The updated observation error variance r( , or the updated modeling error variance q( T), and the prepared smoothing solution S−( T) are transferred to the maximum likelihood estimation part 250 (step 1206).
As shown in FIG. 13, the maximum likelihood estimation part 250 obtains a power spectrum Z( ,T) after noise suppression of the voice frame T from the noise suppression part 230 (step 1301), and observation error variance r( modeling error variance q( T), and smoothing solution S−( ,T) in the voice frame T from the variance measurement part 240 (step 1302).
Then, by using each of the obtained data, the maximum likelihood estimation part 250 calculates a maximum likelihood estimation value T) by the equation 13 (step 1303). The calculated maximum likelihood estimation part T) is transferred to the speech recognition part 260 (step 1304).
In FIG. 14, two microphones 1401, 1402 correspond to the voice input part 210 shown in FIG. 10, and main beam former 1403, and a sub-beam former 1404 realize functions of the sound source localization part 220 and the noise suppression part 230. That is, this 2-channel spectral subtraction beam former executes spectral-subtraction of an output of the sub-beam former 1404 that forms a directional null on a target sound source direction from an output of the main beam former 1403 having directivity pattern on the target sound source direction regarding voices recorded by the two microphones 1401, 1402. The sub-beam former 1404 is considered to output a signal of only a noise component including no voice signals of the target sound source. Each of the outputs of the main beam former 1403 and the sub-beam former 1404 is treated by fast Fourier transformation (FFT). After given predetermined weight W( ) is incorporated and the subtraction is executed, the above is passed through processes of the variance measurement part 240, the maximum likelihood estimation part 250, and executed to inverse fast Fourier transformation (I-FFT) to be outputted to the speech recognition part 260. Needless to say, if the speech recognition part 260 receives data of a frequency domain as an input, this inverse Fourier transformation can be omitted.
An output power spectrum of the main beam former 1403 is set to M1( T), and an output power spectrum of the sub-beam former 1404 is set to M2( T). If a signal power and a noise power included in the main beam former 1403 are respectively S and N1, and a noise power included in the sub-beam former is N2, the following relation is provided.
M 1( T)=S( ,T)+N 1( T)
M 2( T)=N 2( T)
If an output of the sub-beam former 1404 is multiplied by a weight coefficient W( to be subtracted from an output of the main beam former 1403, its output Z is represented as follows.
A weight W is trained to minimize the following by using E[ ] as an expected value operator.
E[[N1( T)−W( ) T)]2]
FIG. 15 shows an example of a trained weight coefficient W( when a noise source is arranged on the right by 40 degrees.
Referring to FIG. 15, it can be understood that an especially large value is determined at a specific frequency. At such a frequency, cancellation accuracy of a noise component expected in the above-described equation is considerably reduced. In other words, a large error occurs accompanying in a value of the observed output power Z( T).
Thus, if there are no large errors in the value of the output power Z( T), i.e., if almost no noise by aliasing is included in a signal of a recorded voice, a maximum likelihood estimation value near an observation value is treated by an inverse fast Fourier transformation to be outputted to the speech recognition part 260. On the other hand, if a large error is present in the value of the output power Z( T), i.e., if much noise by aliasing is included in the signal of the recorded voice, around a specific frequency causing the aliasing, a maximum likelihood estimation value near a smoothing solution is treated by an inverse fast Fourier transformation to be outputted to the speech recognition part 260.
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