Abstract
Obesity is a public health problem in the US. Diet tracking helps control obesity but manual entry is tedious. Proposed solutions such as food recognition from photographs and scanning barcodes have limitations. We investigate the use of speech recognition for diet recording. We improved the accuracy of food order recognition at restaurants by (1) limiting words in the speech recognizer’s corpus to only items on the menus of nearby restaurants (2) implementing an acoustic model to recognize the speaking style of the smartphone owner. Building on these mechanisms, we propose DietRecord, a smartphone application that automatically recognizes and records restaurant orders. Results of our user studies to evaluate DietRecord were encouraging.
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Huang, X., Agu, E. (2016). A Speech-Based Mobile App for Restaurant Order Recognition and Low-Burden Diet Tracking. In: Zheng, X., Zeng, D., Chen, H., Leischow, S. (eds) Smart Health. ICSH 2015. Lecture Notes in Computer Science(), vol 9545. Springer, Cham. https://doi.org/10.1007/978-3-319-29175-8_31
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DOI: https://doi.org/10.1007/978-3-319-29175-8_31
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