How to make active interactions in Japanese as a second language.

This paper analyzes Japanese language classes at Dalarna University in Sweden that are held through a web conferencing system. It discusses how students’ learning and language acquisition can be supported by making better use of the available features of using a web conferencing system for language lessons. Of particular interest is the existence of an “information gap” among students, created because of the limits posed by distance communication. Students who take Japanese courses at Dalarna University usually access classes from their home, which are located all over Sweden or even abroad. This fact can be utilized in language classes because the “information gap” can lead to interactions that are essential for language learning. In order to make use of this natural “information gap” and turn it into an opportunity for communication, our classes used a teaching method called “personalization” [Kawaguchi, 2004].  “Personalization” aims to persuade students to express their own ideas, opinions, feelings and preferences. The present analysis suggests that “personalization” in web-based language classes is a surprisingly effective teaching method. By making students explain about things at home (why they have them, what they use them for, or why they are important), students become motivated to express themselves in Japanese. This makes communication meaningful and enhances students’ interest in improving their vocabulary. Furthermore, by knowing each other, it becomes easier to create a ”supportive classroom environment” [Nuibe, 2001] in which students feel able to express themselves. The analysis suggests that that web-based education can be seen not simply as a supplement to traditional face-to face classroom education, but as a unique and effective educational platform in itself.

Assessment of PD Speech Anomalies @ Home

Background: Voice processing in real-time is challenging. A drawback of previous work for Hypokinetic Dysarthria (HKD) recognition is the requirement of controlled settings in a laboratory environment. A personal digital assistant (PDA) has been developed for home assessment of PD patients. The PDA offers sound processing capabilities, which allow for developing a module for recognition and quantification HKD. Objective: To compose an algorithm for assessment of PD speech severity in the home environment based on a review synthesis. Methods: A two-tier review methodology is utilized. The first tier focuses on real-time problems in speech detection. In the second tier, acoustics features that are robust to medication changes in Levodopa-responsive patients are investigated for HKD recognition. Keywords such as Hypokinetic Dysarthria , and Speech recognition in real time were used in the search engines. IEEE explorer produced the most useful search hits as compared to Google Scholar, ELIN, EBRARY, PubMed and LIBRIS. Results: Vowel and consonant formants are the most relevant acoustic parameters to reflect PD medication changes. Since relevant speech segments (consonants and vowels) contains minority of speech energy, intelligibility can be improved by amplifying the voice signal using amplitude compression. Pause detection and peak to average power rate calculations for voice segmentation produce rich voice features in real time. Enhancements in voice segmentation can be done by inducing Zero-Crossing rate (ZCR). Consonants have high ZCR whereas vowels have low ZCR. Wavelet transform is found promising for voice analysis since it quantizes non-stationary voice signals over time-series using scale and translation parameters. In this way voice intelligibility in the waveforms can be analyzed in each time frame. Conclusions: This review evaluated HKD recognition algorithms to develop a tool for PD speech home-assessment using modern mobile technology. An algorithm that tackles realtime constraints in HKD recognition based on the review synthesis is proposed. We suggest that speech features may be further processed using wavelet transforms and used with a neural network for detection and quantification of speech anomalies related to PD. Based on this model, patients' speech can be automatically categorized according to UPDRS speech ratings.