| Paper ID | E-2-2.4 |
| Paper Title |
A DATA AUGMENTATION TECHNIQUE FOR AUTOMATIC DETECTION OF CHEWING SIDE AND SWALLOWING |
| Authors |
Akihiro Nakamura, Shizuoka University, Japan; Takato Saito, Daizo Ikeda, Ken Ohta, NTT DOCOMO, INC., Japan; Hiroshi Mineno, Masafumi Nishimura, Shizuoka University, Japan |
| Session |
E-2-2: Speech Analysis |
| Time | Wednesday, 09 December, 15:30 - 17:00 |
| Presentation Time: | Wednesday, 09 December, 16:15 - 16:30 Check your Time Zone |
|
All times are in New Zealand Time (UTC +13) |
| Topic |
Speech, Language, and Audio (SLA): |
| Abstract |
Poor quality of eating behavior is known to have adverse effects on health. With a view to promoting health, this study examines a monitoring system for eating behavior that uses a convenient microphone. We previously performed automatic detection of masticatory balance and swallowing using two-channel microphone recordings and the Hybrid CTC/Attention Model to detect the quality of eating behavior. In this paper, we propose an N-gram based data augmentation technique using a large amount of weakly labeled data to improve the accuracy of automatic detection. The application of this method to the Hybrid CTC/Attention Model resulted in improved detection performance. Moreover, the performance of open foods not included in the training data was shown to be similar to that of closed foods. |