The present study, by use of questionnaire and vocabulary tests, has investigated the foreign language vocabulary learning situation of 481 undergraduates in terms of their perspective of vocabulary learning, strategy use and vocabulary size. Based on the questionnaire investigation and vocabulary level tests, the characteristics of the subjects’ foreign language vocabulary learning have been o...

Automatically classifying the tissues types of Region of Interest (ROI) in medical imaging has been an important application in Computer-Aided Diagnosis (CAD), such as classification of breast parenchymal tissue in the mammogram, classify lung disease patterns in High-Resolution Computed Tomography (HRCT) etc. Recently, bag-of-features method has shown its power in this field, treating each ROI...

Tracking a digital pursuit rotor task was used to measure dual task costs of language production by young and older adults. Tracking performance by both groups was affected by dual task demands: time on target declined and tracking error increased as dual task demands increased from the baseline condition to a moderately demanding dual task condition to a more demanding dual task condition. Whe...

A recent trend in automatic speech recognition systems is the use of continuous mixture-density hidden Markov models (HMM’s). Despite the good recognition performance that these systems achieve on average in large vocabulary applications, there is a large variability in performance across speakers. Performance degrades dramatically when the user is radically different from the training populati...

A surprising discovery in recent years is that the structure of the adult human brain changes when a new cognitive or motor skill is learned. This effect is seen as a change in local gray or white matter density that correlates with behavioral measures. Critically, however, the cognitive and anatomical mechanisms underlying these learning-related structural brain changes remain unknown. Here, w...

Today, most of the state-of-the-art speech recognizers are based on Hidden Markov modeling. Using semi-continuous or continuous density Hidden Markov Models, the computation of emission probabilities requires the evaluation of mixture Gaussian probability density functions. Since it is very expensive to evaluate all the Gaussians of the mixture density codebook, many recognizers only compute th...

This paper seeks to examine multilingual children’s Javanese across different grades of primary schools with a focus on proficiency, vocabulary, and grammatical development. The data were partially taken from CBLING (Corpus Bilingual Learners’ Languages), corpus developed by Universitas Negeri Malang. Using AntConc as development tool, we measured proficiency using the Mean Length Utterances (M...

This work presents experiments on four segmental training algorithms for mixture density HMMs. The segmental versions of SOM and LVQ3 suggested by the author are compared against the conventional segmental K-means and the segmental GPD. The recognition task used as a test bench is the speaker dependent, but vocabulary independent automatic speech recognition. The output density function of each...

A method is proposed to incorporate mixture density splitting into the acoustic model discriminative training for speech recognition. The standard method is to obtain a high resolution acoustic model by maximum likelihood training and density splitting, and then improving this model by discriminative training. We choose a log-linear form of acoustic model because for a single Gaussian density p...

In [1], we described how to improve Semi-Continuous Density Hidden Markov Models (SC-HMMs) to be as fast as Continuous Density HMMs (CD-HMMs), whilst outperforming them on large vocabulary recognition tasks with context independent models. In this paper, we extend our work with SC-HMMs to context dependent modelling. We propose a novel node splitting criterion in an approach with phonetic decis...