Vowel Recognition from Articulatory Position Time-Series Data

A new approach of recognizing vowels from articulatory position time-series data was proposed and tested in this paper. This approach directly mapped articulatory position time-series data to vowels without extracting articulatory features such as mouth opening. The input time-series data were time-normalized and sampled to fixed-width vectors of articulatory positions. Three commonly used classifiers, Neural Network, Support Vector Machine and Decision Tree were used and their performances were compared on the vectors. A single speaker dataset of eight major English vowels acquired using Electromagnetic Articulograph (EMA) AG500 was used. Recognition rate using cross validation ranged from 76.07% to 91.32% for the three classifiers. In addition, the trained decision trees were consistent with articulatory features commonly used to descriptively distinguish vowels in classical phonetics. The findings are intended to improve the accuracy and response time of a real-time articulatory-to-acoustics synthesizer

A Bimanual Flick-Based Japanese Tablet Keyboard Using Direct Kanji Input

Tablets, as well as smartphones and personal computers, are popular as Internet clients. A split keyboard is a software keyboard suitable for tablets with large screens. However, unlike other methods, the split keyboard has space in the center of the screen, which makes the part of the screen for displaying suggestions small and inconvenient. This paper proposes a Japanese input software keyboard that enables direct kanji input on a split flick keyboard. Once the user has mastered this keyboard, it allows the user to efficiently input Japanese text while holding a tablet with both hands. The paper presents an implementation of the keyboard on Android and reports the results of three types of experiments, a comparative experiment, an evaluation experiment, and a long-term experiment. The comparative experiment made it clear that it was difficult to evaluate the performance of the proposed keyboard according to the experiment based on short-time practice. The evaluation experiment examined the degree of its mastery after the use of a learning facility. The long-term experiment was conducted by the author to confirm the possibility of its mastery. For 15 months, both the input speed and the error rate have gradually improved. These results suggest that the performance could be improved by learning