The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
With One Voice: Composing a Travel Voice Assistant from Repurposed Models
Open accessAbstract
Voice assistants provide users a new way of interacting with digital products, allowing them to retrieve information and complete tasks with an increased sense of control and flexibility. Such products are comprised of several machine learning models, like Speech-to-Text transcription, Named Entity Recognition and Resolution, and Text Classification. Building a voice assistant from scratch takes the prolonged efforts of several teams constructing numerous models and orchestrating between components. Alternatives such as using third-party vendors or re-purposing existing models may be considered to shorten time-to-market and development costs. However, each option has its benefits and drawbacks. We present key insights from building a voice search assistant for Booking.com. Our paper compares the achieved performance and development efforts in dedicated tailor-made solutions against existing re-purposed models. We share and discuss our data-driven decisions about implementation trade-offs and their estimated outcomes in hindsight, showing that a fully functional Machine-Learning product can be built from existing models.
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“questionnaires can be used for more informative communication, they are mostly fixed and lack adaptability for different individual conditions [65,89]”
From Talk2Care: An LLM-based Voice Assistant for Communication between Healthcare Providers and Older Adults (2024) · cites this paper as [89]
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Streaming End-to-end Speech Recognition for Mobile Devices
He, Y., Sainath, T. N., Prabhavalkar, R., McGraw, I., Alvarez, R., Zhao, D., Rybach, D., Kannan, A., Wu, Y., Pang, R., et al. (2019) · ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2019.8682336 · Cited by 601
Searching by Talking: Analysis of Voice Queries on Mobile Web Search
Guy, I. (2016) · Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval · doi:10.1145/2911451.2911525 · Cited by 140
150 Successful Machine Learning Models: 6 Lessons Learned at Booking.com
Bernardi, L., Mavridis, T., & Estevez, P. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330744 · Cited by 95
Research works printed but not linked: 25
- Etienne Barnard Johan Schalkwyk Charl Van Heerden and Pedro J Moreno. 2010. Voice search for development. (2010). Etienne Barnard Johan Schalkwyk Charl Van Heerden and Pedro J Moreno. 2010. Voice search for development. (2010). · DOI
- 10.1145/3264901 · DOI · Paper page
- 10.1016/j.infsof.2019.03.015 · DOI
- Eric Corbett and Astrid Weber . 2016. What Can I Say? Addressing User Experience Challenges of a Mobile Voice User Interface for Accessibility(MobileHCI ’16) . Association for Computing Machinery , New York, NY, USA , 72–82. Eric Corbett and Astrid Weber. 2016. What Can I Say? Addressing User Experience Challenges of a Mobile Voice User Interface for Accessibility(MobileHCI ’16). Association for Computing Machinery, New York, NY, USA, 72–82.
- Yang Fuqing , Mei Hong , and Li Keqin . 1999. Software Reuse and Software Component Technology [J]. Acta Electronica Sinica 2 ( 1999 ). Yang Fuqing, Mei Hong, and Li Keqin. 1999. Software Reuse and Software Component Technology [J]. Acta Electronica Sinica 2 (1999).
- 10.1145/3437963.3441657 · DOI
- 10.1145/3292500.3330658 · DOI
- 10.1109/ICASSP.2013.6639348 · DOI
- Johannes Hoffart , Mohamed Amir Yosef , Ilaria Bordino , Hagen Fürstenau , Manfred Pinkal , Marc Spaniol , Bilyana Taneva , Stefan Thater , and Gerhard Weikum . 2011 . Robust disambiguation of named entities in text . In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 782–792 . Johannes Hoffart, Mohamed Amir Yosef, Ilaria Bordino, Hagen Fürstenau, Manfred Pinkal, Marc Spaniol, Bilyana Taneva, Stefan Thater, and Gerhard Weikum. 2011. Robust disambiguation of named entities in text. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. 782–792.
- Matthew B Hoy . 2018. Alexa, Siri, Cortana, and more : an introduction to voice assistants. Medical reference services quarterly 37, 1 ( 2018 ), 81–88. Matthew B Hoy. 2018. Alexa, Siri, Cortana, and more: an introduction to voice assistants. Medical reference services quarterly 37, 1 (2018), 81–88.
- 10.1145/3109859.3109873 · DOI
- 10.1109/CCWC.2018.8301638 · DOI
- 10.18653/v1/D19-1676 · DOI
- Takashi Kimura , Takashi Nose , Shinji Hirooka , Yuya Chiba , and Akinori Ito . 2018 . Comparison of speech recognition performance between Kaldi and Google cloud speech API . In International Conference on Intelligent Information Hiding and Multimedia Signal Processing. Springer, 109–115 . Takashi Kimura, Takashi Nose, Shinji Hirooka, Yuya Chiba, and Akinori Ito. 2018. Comparison of speech recognition performance between Kaldi and Google cloud speech API. In International Conference on Intelligent Information Hiding and Multimedia Signal Processing. Springer, 109–115.
- 10.1145/2487575.2488217 · DOI
- Pavel Levin Nishikant Dhanuka and Maxim Khalilov. 2017. Machine translation at booking.com: Journey and lessons learned. arXiv preprint arXiv:1707.07911(2017). Pavel Levin Nishikant Dhanuka and Maxim Khalilov. 2017. Machine translation at booking.com: Journey and lessons learned. arXiv preprint arXiv:1707.07911(2017). · arXiv
- 10.1109/COMPSAC.2007.211 · DOI
- Alex Mari . 2019 . Voice Commerce: Understanding shopping-related voice assistants and their effect on brands. (2019). Alex Mari. 2019. Voice Commerce: Understanding shopping-related voice assistants and their effect on brands. (2019).
- Themis Mavridis , Soraya Hausl , Andrew Mende , and Roberto Pagano . 2020. Beyond algorithms: Ranking at scale at Booking. com . In ComplexRec 2020 , Recommendation in Complex Scenarios and the Impact of Recommender Systems . Themis Mavridis, Soraya Hausl, Andrew Mende, and Roberto Pagano. 2020. Beyond algorithms: Ranking at scale at Booking. com. In ComplexRec 2020, Recommendation in Complex Scenarios and the Impact of Recommender Systems.
- 10.1109/TKDE.2009.191 · DOI · Paper page
- 10.1109/TSE.2017.2677909 · DOI
- Johan Schalkwyk , Doug Beeferman , Françoise Beaufays , Bill Byrne , Ciprian Chelba , Mike Cohen , Maryam Kamvar , and Brian Strope . 2010. “ your word is my command”: Google search by voice: A case study . In Advances in speech recognition . Springer , 61–90. Johan Schalkwyk, Doug Beeferman, Françoise Beaufays, Bill Byrne, Ciprian Chelba, Mike Cohen, Maryam Kamvar, and Brian Strope. 2010. “your word is my command”: Google search by voice: A case study. In Advances in speech recognition. Springer, 61–90.
- David Sculley , Gary Holt , Daniel Golovin , Eugene Davydov , Todd Phillips , Dietmar Ebner , Vinay Chaudhary , Michael Young , Jean-Francois Crespo , and Dan Dennison . 2015. Hidden technical debt in machine learning systems. Advances in neural information processing systems 28 ( 2015 ), 2503–2511. David Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015. Hidden technical debt in machine learning systems. Advances in neural information processing systems 28 (2015), 2503–2511.
- 10.1109/VL/HCC50065.2020.9127277 · DOI
- Ye-Yi Wang , A. Acero , and C. Chelba . 2003. Is word error rate a good indicator for spoken language understanding accuracy . In 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721) . Ye-Yi Wang, A. Acero, and C. Chelba. 2003. Is word error rate a good indicator for spoken language understanding accuracy. In 2003 IEEE Workshop on Automatic Speech Recognition and Understanding (IEEE Cat. No.03EX721).
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Sorted by citations
Yang, Z., Xu, X., Yao, B., Rogers, E., Zhang, S., Intille, S., Shara, N., Gao, G. G., & Wang, D. (2024) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3659625 · Cited by 120
Metadata
- Authors
- Poran, S., Amsalem, G., Beka, A., & Goldenberg, D.
- Affiliations
- Booking.com, Israel
- ORCID
- Goldenberg, Dmitri
- Venue
- Companion Proceedings of the Web Conference 2022
- Publisher
- ACM
- Published
- 2022-04-25
- Pages
- 383-387
- OpenAlex
- W3194914904
- Cited by
- 4
- References
- 3
- Citations by year
- 2025: 1 · 2024: 2 · 2023: 1
- FWCI
- 0.55
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- International World Wide Web Conference (WWW) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- voice assistants, voice search, speech-to-text, named entity recognition, entity resolution, text classification, Booking.com, time to market, development costs
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