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Different layer can have different neurons, but each layer has fixed amount of for bias vector.
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Therefore, we must derive a proper weight matrix W for the bias vector, when (64) is applied to the WLS algorithm.
where c y is a bias vector for the target speaker.
Therefore we only use the magnitude of r x ̂ ( t − 1 ) for calculating the estimated bias vector.
from Equation 17, where c x is a bias vector of forward inference for the source speaker.
where b y and σ y are a bias vector of backward inference for the target speaker, respectively.
the bias vector including start off biases and bias residuals for gyroscopes.
the bias vector including start off biases and bias residuals for accelerometers.
For the SVM-T, the MLLR transforms resulted in 2,070-dimensional 2,070-dimensional45×45+45, including the bias vector b).
the gyro and accelerometer's bias vector.
where b ̂ ( t ) is the estimated bias vector.
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