A Hand-Held Multimedia Translation and Interpretation System with Application to Diet Management

We propose a network independent, hand-held system to translate and disambiguate foreign restaurant menu items in real-time. The system is based on the use of a portable multimedia device, such as a smartphones or a PDA. An accurate and fast translation is obtained using a Machine Translation engine and a context-specific corpora to which we apply two pre-processing steps, called translation standardization and $n$-gram consolidation. The phrase-table generated is orders of magnitude lighter than the ones commonly used in market applications, thus making translations computationally less expensive, and decreasing the battery usage. Translation ambiguities are mitigated using multimedia information including images of dishes and ingredients, along with ingredient lists. We implemented a prototype of our system on an iPod Touch Second Generation for English speakers traveling in Spain. Our tests indicate that our translation method yields higher accuracy than translation engines such as Google Translate, and does so almost instantaneously. The memory requirements of the application, including the database of images, are also well within the limits of the device. By combining it with a database of nutritional information, our proposed system can be used to help individuals who follow a medical diet maintain this diet while traveling.

[1]  K. Kolasa Nutrition in the Prevention and Treatment of Disease , 2018 .

[2]  Edward J. Delp,et al.  A hand-held multimedia translation and interpretation system for diet management , 2011, 2011 IEEE International Conference on Multimedia and Expo.

[3]  F. Sheppard Medical writing in English: The problem with Google Translate. , 2011, Presse medicale.

[4]  Rob Miller,et al.  Crowdsourced Databases: Query Processing with People , 2011, CIDR.

[5]  Adeeba Kamarulzaman,et al.  HIV prevention, treatment, and care services for people who inject drugs: a systematic review of global, regional, and national coverage , 2010 .

[6]  Chris Callison-Burch,et al.  Creating Speech and Language Data With Amazon’s Mechanical Turk , 2010, Mturk@HLT-NAACL.

[7]  David S. Ebert,et al.  The Use of Mobile Devices in Aiding Dietary Assessment and Evaluation , 2010, IEEE Journal of Selected Topics in Signal Processing.

[8]  Matthew Hickman,et al.  HIV prevention, treatment, and care services for people who inject drugs: a systematic review of global, regional, and national coverage , 2010, The Lancet.

[9]  Tom Baranowski,et al.  Need for technological innovation in dietary assessment. , 2010, Journal of the American Dietetic Association.

[10]  E. Delp,et al.  Evidence-based development of a mobile telephone food record. , 2010, Journal of the American Dietetic Association.

[11]  Translation and testing of the cardiac diet self-efficacy scale for use with Taiwanese older adults. , 2009, Public health nursing.

[12]  Chris Callison-Burch,et al.  Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk , 2009, EMNLP.

[13]  Xiaojun Wan,et al.  Co-Training for Cross-Lingual Sentiment Classification , 2009, ACL.

[14]  Philipp Koehn,et al.  Findings of the 2009 Workshop on Statistical Machine Translation , 2009, WMT@EACL.

[15]  E J Delp,et al.  Use of technology in children’s dietary assessment , 2009, European Journal of Clinical Nutrition.

[16]  Xiaojun Wan,et al.  Using Bilingual Knowledge and Ensemble Techniques for Unsupervised Chinese Sentiment Analysis , 2008, EMNLP.

[17]  Shankar Kumar,et al.  Lattice Minimum Bayes-Risk Decoding for Statistical Machine Translation , 2008, EMNLP.

[18]  Simon M. Lucas,et al.  Learning Finite-State Transducers: Evolution Versus Heuristic State Merging , 2007, IEEE Transactions on Evolutionary Computation.

[19]  Philipp Koehn,et al.  Factored Translation Models , 2007, EMNLP.

[20]  Philipp Koehn,et al.  Moses: Open Source Toolkit for Statistical Machine Translation , 2007, ACL.

[21]  Hermann Ney,et al.  Efficient Phrase-Table Representation for Machine Translation with Applications to Online MT and Speech Translation , 2007, NAACL.

[22]  Federico Gaspari The Added Value of Free Online MT Services: Confidence Boosters for Linguistically-challenged Internet Users, a Case Study for the Language Pair Italian-English , 2006 .

[23]  M. Vidyasagar,et al.  The Realization Problem for Hidden Markov Models: The Complete Realization Problem , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[24]  Heather Y. Lovelace,et al.  Nutrition in the Prevention and Treatment of Disease , 2003 .

[25]  Hermann Ney,et al.  Word Reordering and a Dynamic Programming Beam Search Algorithm for Statistical Machine Translation , 2003, CL.

[26]  Hermann Ney,et al.  A Systematic Comparison of Various Statistical Alignment Models , 2003, CL.

[27]  Andreas Stolcke,et al.  SRILM - an extensible language modeling toolkit , 2002, INTERSPEECH.

[28]  Andreas Stolcke,et al.  Finding consensus in speech recognition: word error minimization and other applications of confusion networks , 2000, Comput. Speech Lang..

[29]  Robert L. Mercer,et al.  The Mathematics of Statistical Machine Translation: Parameter Estimation , 1993, CL.

[30]  Fred Karlsson,et al.  Constraint Grammar as a Framework for Parsing Running Text , 1990, COLING.

[31]  A. Nádas Hidden Markov chains, the forward-backward algorithm, and initial statistics , 1983 .