Search Results (2)

Speaking and eating are essential components of health management. To enable the daily monitoring of these behaviors, systems capable of simultaneously recognizing speech and eating behaviors are required. However, due to the distinct acoustic and contextual characteristics of these two domains, achieving high-precision integrated recognition remains underexplored. In this study, we propose a method that combines data augmentation through synthetic data creation with a two-stage fine-tuning approach tailored to the complexity of domain adaptation. By concatenating speech and eating sounds of varying lengths and sequences, we generated training data that mimic real-world environments where speech and eating behaviors co-exist. Additionally, efficient model adaptation was achieved through two-stage fine-tuning of the self-supervised learning model. The experimental evaluations demonstrate that the proposed method maintains speech recognition accuracy while achieving high detection performance for eating behaviors, with an F1 score of 0.918 for chewing detection and 0.926 for swallowing detection. These results underscore the potential of using voice recognition technology for daily health monitoring. Full article

Communicating under extreme noise conditions remains challenging in spite of higher-order noise-canceling microphones, throat microphones, and signal processing. Both natural and human-made background ambient noise can disturb the conveyance of information because of high noise levels. Noise cancellation, which is used frequently in audio technology, has limits in noise reduction and does not guarantee clear vocal pickup in these severe situations. A contact microphone that is attached directly to the medium of interest has the potential to pick up vocal signals with reduced noise. In this study, an electrostatic transducer with an elastomer layer that is impedance-matched to the human body is used to pick up speech sounds through constant contact on the chin and cheek. By attaching the wearable device directly to the skin, the medium of air is bypassed, and airborne noise is passively canceled. Because of the acoustic impedance-matched layer, the sensor is more sensitive to low frequencies under 500 Hz, so frequency equalization was implemented to flatten the frequency response throughout the vocal range. The perceptual evaluation of speech quality (PESQ) scores of the wearable device with equalization averaged around 2.6 on a scale from –0.5 to 4.5. Speech recordings were also collected in a noise field of 85 dB, and the performance was compared to a cardioid lapel mic, a cardioid dynamic mic, and an omnidirectional condenser mic. The recordings revealed a significantly reduced presence of white noise in the contact sensor. This study provides preliminary results that show potential vocal applications for a wearable impedance-matched sensor. Full article