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The signal processing to extract the formant and pitch values is based on a Linear Prediction Coefficients (LPCs) analysis of the segments considered as vowels in a Hidden Markov Model (HMM) based Viterbi forced alignment.
The signal processing to extract the formant and pitch values is based on a linear prediction coefficient (LPC) analysis of the segments considered as vowels in a hidden Markov model- (HMM-) based Viterbi forced alignment.
The learning curve for AUC used in this study is based on a linear prediction model (online supplementary appendix 1).
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Among the four types of LLL models, LLR prediction is based on a linear regression model with a single explanatory variable.
The predictions are based on a linear kernel SVM classifier with the default parameter C=5 and using our modified 4+1-fold cross validation on the training dataset (see Supplement for details).
Genomic prediction of breeding values can be based on a linear mixed model using matrix computations or a non-linear mixture type of model using Markov chain Monte Carlo (McMC) procedures.
Linear prediction calculates future values of a signal in discrete time format based on a linear function of previous samples.
The training of the SVM based on a linear kernel enabled us to identify the contributions of each feature to an indel prediction.
Based on a linear regression model, it drops about 3.5% for each typo noticed.
A reverberation compensation method for speaker recognition using spectral subtraction [29], in which late reverberation is treated as additive noise, was proposed in [4], while a method based on multistep linear prediction (MSLP) was proposed in [5,17] for both single and multiple microphones.
The speech/non-speech segmentation is implemented using a multi-layer perceptron (MLP) based on perceptual linear prediction (PLP) features, followed by a finite state machine.
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