Pathfinding in hospitals is challenging for patients, visitors, and even employees. Many people have experienced getting lost due to lack of clear guidance, large footprint of hospitals, and confusing array of hospital wings. In this paper, we propose Halo; An indoor navigation application based on voice-user interaction to help provide directions for users without assistance of a localization system. The main challenge is accurate detection of origin and destination search terms. A custom convolutional neural network (CNN) is proposed to detect origin and destination search terms from transcription of a submitted speech query. The CNN is trained based on a set of queries tailored specifically for hospital and clinic environments. Performance of the proposed model is studied and compared with Levenshtein distance-based word matching.
@article{arxiv.1708.02238,
title = {A Convolutional Neural Network for Search Term Detection},
author = {Hojjat Salehinejad and Joseph Barfett and Parham Aarabi and Shahrokh Valaee and Errol Colak and Bruce Gray and Tim Dowdell},
journal= {arXiv preprint arXiv:1708.02238},
year = {2017}
}
Comments
This paper is accepted for presentation at 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications