156,344 research outputs found
Data mining technology for the evaluation of learning content interaction
Interactivity is central for the success of learning. In e-learning and other educational multimedia environments, the evaluation of interaction and behaviour is particularly crucial. Data mining – a non-intrusive, objective analysis technology – shall be proposed as the central evaluation technology for the analysis of the usage of computer-based educational environments and in particular of the interaction with educational content. Basic mining techniques are reviewed and their application in a Web-based third-level course environment is illustrated. Analytic models capturing interaction aspects from the application domain (learning) and the software infrastructure (interactive multimedia) are required for the meaningful interpretation of mining results
Articulatory and bottleneck features for speaker-independent ASR of dysarthric speech
The rapid population aging has stimulated the development of assistive
devices that provide personalized medical support to the needies suffering from
various etiologies. One prominent clinical application is a computer-assisted
speech training system which enables personalized speech therapy to patients
impaired by communicative disorders in the patient's home environment. Such a
system relies on the robust automatic speech recognition (ASR) technology to be
able to provide accurate articulation feedback. With the long-term aim of
developing off-the-shelf ASR systems that can be incorporated in clinical
context without prior speaker information, we compare the ASR performance of
speaker-independent bottleneck and articulatory features on dysarthric speech
used in conjunction with dedicated neural network-based acoustic models that
have been shown to be robust against spectrotemporal deviations. We report ASR
performance of these systems on two dysarthric speech datasets of different
characteristics to quantify the achieved performance gains. Despite the
remaining performance gap between the dysarthric and normal speech, significant
improvements have been reported on both datasets using speaker-independent ASR
architectures.Comment: to appear in Computer Speech & Language -
https://doi.org/10.1016/j.csl.2019.05.002 - arXiv admin note: substantial
text overlap with arXiv:1807.1094
Improving the translation environment for professional translators
When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side.
This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project
- …