Sentence examples for maximum linear regression from inspiring English sources

The maximum linear regression at the coast reached -391.83 m during that period.

Some examples of the model adaptation method include the (constrained) maximum likelihood linear regression (CMLLR/MLLR) [30], maximum a posteriori (MAP) [37], and vector Taylor series (VTS) [38 40] based adaptation methods.

This latter method exploits an invaluable prior knowledge attained from an average voice model [3], and adapts this general model using an adaptation algorithm such as maximum likelihood linear regression (MLLR)[32], maximum a posteriori (MAP)[33], and cluster adaptive training (CAT [21].

The data sparseness problem is significant when the training data are limited, which is often solved by adaptation techniques such as maximum a posteriori (MAP) [24], maximum likelihood linear regression (MLLR) [25], and eigenspace-based techniques [26].

Various forms of speaker adaptation techniques like maximum a posteriori (MAP) and maximum likelihood linear regression (MLLR) [13], speaker adaptive training (SAT) [20, 21], constrained MLLR speaker normalization (CMLSN) [22], and their combinations [13] have also been tried so as to reduce the mismatch of children's speech with adults' speech trained models.

Maximum likelihood linear regression (MLLR) [33, 42], maximum a posteriori (MAP) [34, 24], parallel model combination (PMC) [23, 31, 33] and a novel enhanced version of PMC, PCA and CMS based PMC (PC-PMC) [30] are well incorporated in the system.

Some examples of backend approaches are Parallel Model Combination (PMC) [5] and model adaption algorithms, such as Maximum Likelihood Linear Regression (MLLR) [6] and Maximum A Posterior probability (MAP) based adaptation [7].

This is in spirit similar to the popular feature space maximum likelihood linear regression (fMLLR) [30] that uses the maximum likelihood criterion to estimate the linear transform.

The standard adaptation methods used for speaker adaptation, such as maximum a posteriori (MAP) [1, 2] and maximum likelihood linear regression (MLLR) [9], can be also used for environment model adaptation.

It has been shown [16] that SGMMs behave more compact and perform better than the conventional GMMs approach, as well, without the loss of compatibility to most standard techniques such as feature-space adaptation using maximum likelihood linear regression (fMLLR) [53], discriminative training like boosted maximum mutual information (bMMI) [54], or the minimum Bayes risk (MBR) approach [55].

The maximum likelihood linear regression (MLLR) concept [13] can be considered as a generalization of constrained maximum likelihood linear regression (CMLLR) as it allows for a separate transform matrix (mathbf {B}_{k_{n}}phantom {dot {i}!}) in (57): begin{array}rcl@ boldsymbol{mu}_{mathbf{y}|k_{n}} &=& mathbf{A}_{k_{n}} boldsymbol{mu}_{mathbf{x}|k_{n}} + mathbf{b}_{k_{n}}, end{array} (58).