We propose a parameter shrinkage adaptation framework to estimate models with only a limited set of adaptation data to improve accuracy for automatic speech recognition, by regularizing an objective function with a sum of parameterwise power q constraint. For the first attempt, we formulate ridge maximum likelihood linear regression (MLLR) and ridge constraint MLLR (CMLLR) with an element-wise ...

The brain maintains saccade accuracy by modifying saccades that are consistently inaccurate (e.g. hypermetric). To determine whether this adaptation is influenced by the visual background we used several different target and background movements to elicit changes in saccade gain. In almost all cases, the target spot drove gain changes. The background had no effect on, or slightly reduced, adapt...

The oculomotor system maintains saccade accuracy by adjusting saccades that are consistently inaccurate. Four experiments were performed to determine the relative contribution of background and target postsaccadic displacement. Unlike typical saccade adaptation experiments, we used natural image scenes and masked target and background displacements during the saccade to exclude motion signals f...

CRT white point, adapted white point, chromatic adaptation, viewing condition, monitor characterization, psychophysics, neutral point, mixed mode illumination, incomplete adaptation An iterative psychophysical algorithm is designed and implemented to determine the adapted white point for cathode ray tubes (CRTs) in a given viewing condition. This algorithm can accurately determine the chromatic...

The projection-based likelihood measure, an e€ective means of reducing noise contamination in speech recognition, dynamically searches an optimal equalization factor for adapting the cepstral mean vector of hidden Markov model (HMM) to equalize the noisy observation. In this paper, we present a novel likelihood measure which extends the adaptation mechanism to the shrinkage of covariance matrix...

Domain adaptation methods aims to improve the accuracy of the target predictive classifier while using the patterns from a related source domain that has large number of labeled data. To solve the domain adaptation problem, we propose new simple and intuitive method that can improve the learning of target data by calculating the distance of each instances in source and target domain. We added d...

When a word sense disambiguation (WSD) system is trained on one domain but applied to a different domain, a drop in accuracy is frequently observed. This highlights the importance of domain adaptation for word sense disambiguation. In this paper, we first show that an active learning approach can be successfully used to perform domain adaptation of WSD systems. Then, by using the predominant se...

We demonstrate SODA (Service Oriented Domain Adaptation) for efficient and scalable cross-domain microblog categorization which works on the principle of transfer learning. It is developed on a novel similarity-based iterative domain adaptation algorithm while extended with features such as active learning and interactive GUI to be used by business professionals. SODA demonstrates efficient cla...

Background: Temporomandibular joint disorder (TMD) might be manifested as structural changes in bone through modification, adaptation or direct destruction. We propose to use Local Binary Pattern (LBP) characteristics and histogram-oriented gradients on the recorded images as a diagnostic tool in TMD assessment.Material and Methods: CBCT images of 66 patients (132 joints) with TMD and 66 normal...

This paper extends the evaluation of Hidden Markov Models with quantized parameters (qHMM) presented in [5] to the case of speaker adaptive training. In speaker-independent speech recognition tasks, qHMMs were found to provide a similar performance as the original continuous density HMMs (CDHMM) with substantially reduced memory requirements. In this paper, we propose a Bayesian type of adaptat...