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java.lang.Objectedu.cmu.sphinx.linguist.acoustic.tiedstate.MixtureComponent
Defines the set of shared elements for a GaussianMixture. Since
these elements are potentially shared by a number of
GaussianMixtures
,
these elements should not be written to. The
GaussianMixture defines a single probability density function along
with a set of adaptation parameters.
Note that all scores and weights are in LogMath log base
Constructor Summary | |
MixtureComponent(LogMath logMath,
float[] mean,
float[][] meanTransformationMatrix,
float[] meanTransformationVector,
float[] variance,
float[][] varianceTransformationMatrix,
float[] varianceTransformationVector,
float distFloor,
float varianceFloor)
Create a MixtureComponent with the given sub components. |
Method Summary | |
float[] |
getMean()
Returns the mean for this component. |
float |
getScore(Data feature)
Calculate the score for this mixture against the given feature. |
float[] |
getVariance()
Returns the variance for this component. |
float |
precomputeDistance()
Pre-compute factors for the Mahalanobis distance. |
Methods inherited from class java.lang.Object |
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
Constructor Detail |
public MixtureComponent(LogMath logMath, float[] mean, float[][] meanTransformationMatrix, float[] meanTransformationVector, float[] variance, float[][] varianceTransformationMatrix, float[] varianceTransformationVector, float distFloor, float varianceFloor)
logMath
- the log math to usemean
- the mean vector for this PDFmeanTransformationMatrix
- transformation matrix for this pdfmeanTransformationVector
- transform vector for this PDFvariance
- the variance for this PDFvarianceTransformationMatrix
- var. transform matrix for this PDFvarianceTransformationVector
- var. transform vector for this PDFdistFloor
- the lowest score valuevarianceFloor
- the lowest value for the varianceMethod Detail |
public float[] getMean()
public float[] getVariance()
public float getScore(Data feature)
Normal(x) = exp{-0.5 * (x-m)' * inv(Var) * (x-m)} / {sqrt((2 * PI) ^ N) * det(Var))}
where x and m are the incoming cepstra and mean vector respectivally, Var is the Covariance matrix, det() is the determinant of a matrix, inv() is its inverse, exp is the exponential operator, x' is the transposed vector of x and N is the dimension of the vectors x and m.
feature
- the feature to score
public float precomputeDistance()
{sqrt((2 * PI) ^ N) * det(Var))}
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