Sentence examples for mean model adaptation from inspiring English sources

Under this assumption, the HEQ algorithm is applied for mean model adaptation as in (8).

We believe that the superior performance of MLLR at the higher SNRs is also largely resulted from the mean model adaptation.

The experimental results also indicate that the mean model adaptation plays the major role in improving the performance of the speech recognizer in noisy environments.

Similar results are obtained in the MLLR-based mean-only model adaptation experiments.

In the HEQ-MA with mean and variance adaptation approach, we used an SNR-dependent covariance model adaptation technique.

We present the precision differential as a means of assessing how the type of model and level of model adaptation generate variation among model outputs.

The article concentrates on acoustic model adaptation, discriminative training, and additional dithering as prominent means of compensating for the described distortion in the task of phoneme and large vocabulary continuous speech recognition (LVCSR).

In the MLLR-based model adaptation, we adopted the unsupervised adaptation method where the acoustic mean models were incrementally adapted with each test utterance.

In the HEQ-based model adaptation, the HEQ and proposed variance adaptation techniques are applied to the 39-dimensional mean vectors and diagonal covariance matrices, respectively, of all trained HMMs in the baseline speech recognizers on a component-by-component basis.

Model Adaptation for Sentence Segmentation from Speech.

Model Adaptation for Dialog Act Tagging.