research-article

The Characteristics of Voice Search: Comparing Spoken with Typed-in Mobile Web Search Queries

Author:

Ido GuyAuthors Info & Claims

ACM Transactions on Information Systems (TOIS), Volume 36, Issue 3

Article No.: 30, Pages 1 - 28

https://doi.org/10.1145/3182163

Published: 13 March 2018 Publication History

Abstract

The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries rather than type them. While this kind of voice search is still in its infancy, it is gradually becoming more widespread. In this article, we report a comprehensive voice search query log analysis of a commercial web search engine’s mobile application. We compare voice and text search by various aspects, with special focus on the semantic and syntactic characteristics of the queries. Our analysis suggests that voice queries focus more on audio-visual content and question answering and less on social networking and adult domains. In addition, voice queries are more commonly submitted on the go. We also conduct an empirical evaluation showing that the language of voice queries is closer to natural language than the language of text queries. Our analysis points out further differences between voice and text search. We discuss the implications of these differences for the design of future voice-enabled web search tools.

Supplementary Material

JPG File (a30-guy.jpg)

Download
20.48 KB

MP4 File (a30-guy.mp4)

Download
526.64 MB

References

[1]

A. Acero, N. Bernstein, R. Chambers, Y. C. Ju, X. Li, J. Odell, P. Nguyen, O. Scholz, and G. Zweig. 2008. Live search for mobile: Web services by voice on the cellphone. In Proc. ICASSP. 5256--5259.

[2]

Lada A. Adamic, Jun Zhang, Eytan Bakshy, and Mark S. Ackerman. 2008. Knowledge sharing and Yahoo answers: Everyone knows something. In Proc. WWW. 665--674.

Digital Library

[3]

Ahmed Hassan Awadallah, Ranjitha Gurunath Kulkarni, Umut Ozertem, and Rosie Jones. 2015. Characterizing and predicting voice query reformulation. In Proc. CIKM. 543--552.

Digital Library

[4]

Ricardo Baeza-Yates, Georges Dupret, and Javier Velasco. 2007. A study of mobile search queries in Japan. In Query Log Analysis (WWW’07 Workshop).

[5]

Cory Barr, Rosie Jones, and Moira Regelson. 2008. The linguistic structure of English web-search queries. In Proc. EMNLP. 1021--1030.

Digital Library

[6]

Adam Berger and John Lafferty. 1999. Information retrieval as statistical translation. In Proc. SIGIR. 222--229.

Digital Library

[7]

Mikhail Burtsev, Aleksandr Chuklin, Julia Kiseleva, and Alexey Borisov. 2017. Search-oriented conversational AI (SCAI). In Proc. ICTIR. 333--334.

Digital Library

[8]

David Carmel, Avihai Mejer, Yuval Pinter, and Idan Szpektor. 2014. Improving term weighting for community question answering search using syntactic analysis. In Proc. CIKM. 351--360.

Digital Library

[9]

David Carmel, Erel Uziel, Ido Guy, Yosi Mass, and Haggai Roitman. 2012. Folksonomy-based term extraction for word cloud generation. ACM Transactions on Intelligent Systems and Technology 3, 4 (2012), 60:1--60:20.

Digital Library

[10]

Deepayan Chakrabarti, Ravi Kumar, and Kunal Punera. 2009. Quicklink selection for navigational query results. In Proc. WWW. 391--400.

Digital Library

[11]

Barbara L. Chalfonte, Robert S. Fish, and Robert E. Kraut. 1991. Expressive richness: A comparison of speech and text as media for revision. In Proc. CHI. 21--26.

Digital Library

[12]

William Chan, Navdeep Jaitly, Quoc Le, and Oriol Vinyals. 2016. Listen, attend and spell: A neural network for large vocabulary conversational speech recognition. In Proc. ICASSP. IEEE, 4960--4964.

[13]

Ciprian Chelba and Johan Schalkwyk. 2013. Empirical Exploration of Language Modeling for the google.com Query Stream as Applied to Mobile Voice Search. Springer Science+Business Media, New York. 197--229.

[14]

Ciprian Chelba, Xuedong Zhang, and Keith Hall. 2015. Geo-location for voice search language modeling. In Proc. INTERSPEECH, 1438--1442.

[15]

Lydia B. Chilton and Jaime Teevan. 2011. Addressing people’s information needs directly in a web search result page. In Proc. WWW. 27--36.

Digital Library

[16]

Fabio Crestani and Heather Du. 2006. Written versus spoken queries: A qualitative and quantitative comparative analysis. JASIST 57, 7 (2006), 881--890.

Digital Library

[17]

Marie-Catherine De Marneffe, Bill MacCartney, and Christopher D. Manning. 2006. Generating typed dependency parses from phrase structure parses. In Proc. LREC. 449--454.

[18]

Gideon Dror, Yoelle Maarek, Avihai Mejer, and Idan Szpektor. 2013. From query to question in one click: Suggesting synthetic questions to searchers. In Proc. WWW. 391--402.

Digital Library

[19]

Manish Gupta and Michael Bendersky. 2015. Information retrieval with verbose queries. Foundations and Trends in Information Retrieval 9, 3--4 (2015), 209--354.

[20]

Ido Guy, Roy Levin, Tal Daniel, and Ella Bolshinsky. 2015. Islands in the stream: A study of item recommendation within an enterprise social stream. In Proc. of SIGIR. 665--674.

Digital Library

[21]

Ido Guy, Inbal Ronen, Naama Zwerdling, Irena Zuyev-Grabovitch, and Michal Jacovi. 2016. What is your organization ‘like’?: A study of liking activity in the enterprise. In Proc. of CHI. 3025--3037.

Digital Library

[22]

G. Hinton, Li Deng, Dong Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath, and B. Kingsbury. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. Signal Processing Magazine 29, 6 (2012), 82--97.

[23]

Jiepu Jiang, Ahmed Hassan Awadallah, Rosie Jones, Umut Ozertem, Imed Zitouni, Ranjitha Gurunath Kulkarni, and Omar Zia Khan. 2015. Automatic online evaluation of intelligent assistants. In Proc. WWW. 506--516.

Digital Library

[24]

Jiepu Jiang, Wei Jeng, and Daqing He. 2013. How do users respond to voice input errors? Lexical and phonetic query reformulation in voice search. In Proc. SIGIR. 143--152.

Digital Library

[25]

Maryam Kamvar and Shumeet Baluja. 2006. A large scale study of wireless search behavior: Google mobile search. In Proc. CHI. 701--709.

Digital Library

[26]

Maryam Kamvar, Melanie Kellar, Rajan Patel, and Ya Xu. 2009. Computers and iphones and mobile phones, oh my&excl;: A logs-based comparison of search users on different devices. In Proc. WWW. 801--810.

Digital Library

[27]

Julia Kiseleva, Kyle Williams, Ahmed Hassan Awadallah, Aidan C. Crook, Imed Zitouni, and Tasos Anastasakos. 2016a. Predicting user satisfaction with intelligent assistants. In Proc. SIGIR. 45--54.

Digital Library

[28]

Julia Kiseleva, Kyle Williams, Jiepu Jiang, Ahmed Hassan Awadallah, Aidan C. Crook, Imed Zitouni, and Tasos Anastasakos. 2016b. Understanding user satisfaction with intelligent assistants. In Proc. CHIIR. 121--130.

Digital Library

[29]

Dan Klein and Christopher D. Manning. 2003. Accurate unlexicalized parsing. In Proc. ACL. 423--430.

Digital Library

[30]

Elad Kravi, Eugene Agichtein, Ido Guy, Yaron Kanza, Avihai Mejer, and Dan Pelleg. 2015. Searcher in a strange land: Understanding web search from familiar and unfamiliar locations. In Proc. SIGIR. 855--858.

Digital Library

[31]

Elad Kravi, Ido Guy, Avihai Mejer, David Carmel, Yoelle Maarek, Dan Pelleg, and Gilad Tsur. 2016. One query, many clicks: Analysis of queries with multiple clicks by the same user. In Proc. CIKM. 1423--1432.

Digital Library

[32]

Dmitry Lagun, Chih-Hung Hsieh, Dale Webster, and Vidhya Navalpakkam. 2014. Towards better measurement of attention and satisfaction in mobile search. In Proc. SIGIR (SIGIR’14). 113--122.

Digital Library

[33]

Rivka Levitan and David Elson. 2014. Detecting retries of voice search queries. In Proc. ACL. 230--235.

[34]

Jane Li, Scott Huffman, and Akihito Tokuda. 2009. Good abandonment in mobile and PC internet search. In Proc. SIGIR. 43--50.

Digital Library

[35]

Chin-Yew Lin. 2008. Automatic question generation from queries. In Proc. Workshop on the Question Generation Shared Task. 156--164.

[36]

Mitchell P. Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993. Building a large annotated corpus of English: The penn treebank. Computational Linguistics 19, 2 (1993), 313--330.

Digital Library

[37]

George D. Montanez, Ryen W. White, and Xiao Huang. 2014. Cross-device search. In Proc. CIKM. 1669--1678.

Digital Library

[38]

Aarthi Easwara Moorthy and Kim-Phuong L. Vu. 2015. Privacy concerns for use of voice activated personal assistant in the public space. International Journal of Human--Computer Interaction 31, 4 (2015), 307--335.

[39]

A. Moreno-Daniel, S. Parthasarathy, B. H. Juang, and J. G. Wilpon. 2007. Spoken query processing for information retrieval. In Proc. ICASSP, Vol. 4. IV-121--IV-124.

[40]

Yuval Pinter, Roi Reichart, and Idan Szpektor. 2016. Syntactic parsing of web queries with question intent: A distant supervision approach. In Proc. NAACL. 670--680.

[41]

Filip Radlinski and Nick Craswell. 2017. A theoretical framework for conversational search. In Proc. 2017 Conference on Conference Human Information Interaction and Retrieval (CHIIR’17). 117--126.

Digital Library

[42]

Roni Rosenfield. 2000. Two decades of statistical language modeling: Where do we go from here? Proceedings of the IEEE 88, 8 (2000), 1270--1278.

[43]

Shumpei Sano, Nobuhiro Kaji, and Manabu Sassano. 2016. Prediction of prospective user engagement with intelligent assistants. In Proc. ACL. 1203--1212.

[44]

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, Amy Neustein (Ed.). Springer US, 61--90.

[45]

Jiulong Shan, Genqing Wu, Zhihong Hu, Xiliu Tang, Martin Jansche, and Pedro J. Moreno. 2010. Search by voice in Mandarin Chinese. In Proc. INTERSPEECH. 354--357.

[46]

Sosuke Shiga, Hideo Joho, Roi Blanco, Johanne R. Trippas, and Mark Sanderson. 2017. Modelling information needs in collaborative search conversations. In Proc. SIGIR. 715--724.

Digital Library

[47]

Milad Shokouhi and Qi Guo. 2015. From queries to cards: Re-ranking proactive card recommendations based on reactive search history. In Proc. SIGIR. 695--704.

Digital Library

[48]

Milad Shokouhi, Rosie Jones, Umut Ozertem, Karthik Raghunathan, and Fernando Diaz. 2014. Mobile query reformulations. In Proc. SIGIR. 1011--1014.

Digital Library

[49]

Milad Shokouhi, Umut Ozertem, and Nick Craswell. 2016. Did you say u2 or youtube?: Inferring implicit transcripts from voice search logs. In Proc. WWW. 1215--1224.

Digital Library

[50]

Yang Song, Hao Ma, Hongning Wang, and Kuansan Wang. 2013. Exploring and exploiting user search behavior on mobile and tablet devices to improve search relevance. In Proc. WWW. 1201--1212.

Digital Library

[51]

Jaime Teevan, Daniel Ramage, and Merredith Ringel Morris. 2011. #TwitterSearch: A comparison of microblog search and web search. In Proc. WSDM. 35--44.

Digital Library

[52]

Kristina Toutanova, Dan Klein, Christopher D. Manning, and Yoram Singer. 2003. Feature-rich part-of-speech tagging with a cyclic dependency network. In Proc. NAACL. 173--180.

Digital Library

[53]

Gilad Tsur, Yuval Pinter, Idan Szpektor, and David Carmel. 2016. Identifying web queries with question intent. In Proc. WWW. 783--793.

Digital Library

[54]

Suzan Verberne. 2007. Paragraph retrieval for why-question answering. In Proc. SIGIR. 922.

Digital Library

[55]

Ye-Yi Wang, Dong Yu, Yun-Cheng Ju, and A. Acero. 2008. An introduction to voice search. IEEE Signal Processing Magazine, 25, 3 (2008), 28--38.

[56]

Ryen W. White, Matthew Richardson, and Wen-tau Yih. 2015. Questions vs. queries in informational search tasks. In Proc. WWW. 135--136.

Digital Library

[57]

Kyle Williams, Julia Kiseleva, Aidan C. Crook, Imed Zitouni, Ahmed Hassan Awadallah, and Madian Khabsa. 2016. Detecting good abandonment in mobile search. In Proc. WWW. 495--505.

Digital Library

[58]

Zhao Yan, Nan Duan, Jun-Wei Bao, Peng Chen, Ming Zhou, Zhoujun Li, and Jianshe Zhou. 2016. DocChat: An information retrieval approach for chatbot engines using unstructured documents. In Proc. ACL. 516--525.

[59]

Jeonghe Yi and Farzin Maghoul. 2011. Mobile search pattern evolution: The trend and the impact of voice queries. In Proc. WWW. 165--166.

Digital Library

[60]

Jeonghee Yi, Farzin Maghoul, and Jan Pedersen. 2008. Deciphering mobile search patterns: A study of yahoo&excl; mobile search queries. In Proc. WWW. 257--266.

Digital Library

[61]

Chengxiang Zhai and John Lafferty. 2001. A study of smoothing methods for language models applied to ad hoc information retrieval. In Proc. SIGIR (SIGIR’01). 334--342.

Digital Library

[62]

Geoffrey Zweig and Shuangyu Chang. 2011. Personalizing model M for voice-search. In Proc. INTERSPEECH. 609--612.

Cited By

Ji KPathiyan Cherumanal STrippas JHettiachchi DSalim FScholer FSpina D(2024)Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational SearchAdjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction10.1145/3640471.3680245(1-10)Online publication date: 21-Sep-2024
https://dl.acm.org/doi/10.1145/3640471.3680245
Trippas JGallagher LMackenzie JSerra ESpezzano F(2024)Re-evaluating the Command-and-Control Paradigm in Conversational Search InteractionsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679588(2260-2270)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679588
Trippas JAl Lawati SMackenzie JGallagher LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)What do Users Really Ask Large Language Models? An Initial Log Analysis of Google Bard Interactions in the WildProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657914(2703-2707)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657914
Show More Cited By

Index Terms

The Characteristics of Voice Search: Comparing Spoken with Typed-in Mobile Web Search Queries
1. Information systems
  1. Information retrieval
  2. World Wide Web
    1. Web searching and information discovery
      1. Web search engines

Recommendations

Searching by Talking: Analysis of Voice Queries on Mobile Web Search
SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries, rather than type them. While this kind of voice search is still in its infancy, it is ...
Characterizing and Predicting Voice Query Reformulation
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Voice interactions are becoming more prevalent as the usage of voice search and intelligent assistants gains more popularity. Users frequently reformulate their requests in hope of getting better results either because the system was unable to recognize ...
How do users respond to voice input errors?: lexical and phonetic query reformulation in voice search
SIGIR '13: Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval

Voice search offers users with a new search experience: instead of typing, users can vocalize their search queries. However, due to voice input errors (such as speech recognition errors and improper system interruptions), users need to frequently ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 36, Issue 3

July 2018

402 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3146384

Editor:
Maarten de Rijke
University of Amsterdam, The Netherlands

Issue’s Table of Contents

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 March 2018

Accepted: 01 January 2018

Revised: 01 December 2017

Received: 01 April 2017

Published in TOIS Volume 36, Issue 3

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

34
Total Citations
View Citations
1,408
Total Downloads

Downloads (Last 12 months)101
Downloads (Last 6 weeks)8

Reflects downloads up to 24 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Ji KPathiyan Cherumanal STrippas JHettiachchi DSalim FScholer FSpina D(2024)Towards Detecting and Mitigating Cognitive Bias in Spoken Conversational SearchAdjunct Proceedings of the 26th International Conference on Mobile Human-Computer Interaction10.1145/3640471.3680245(1-10)Online publication date: 21-Sep-2024
https://dl.acm.org/doi/10.1145/3640471.3680245
Trippas JGallagher LMackenzie JSerra ESpezzano F(2024)Re-evaluating the Command-and-Control Paradigm in Conversational Search InteractionsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679588(2260-2270)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679588
Trippas JAl Lawati SMackenzie JGallagher LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)What do Users Really Ask Large Language Models? An Initial Log Analysis of Google Bard Interactions in the WildProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657914(2703-2707)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657914
Ponochevnyi NKuzminykh A(2024)Chart What I Say: Exploring Cross-Modality Prompt Alignment in AI-Assisted Chart AuthoringExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650921(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3650921
Shu L(2024)Development of key technologies for audio retrieval and vocal teaching system based on sensor networksMeasurement: Sensors10.1016/j.measen.2024.10104832(101048)Online publication date: Apr-2024
https://doi.org/10.1016/j.measen.2024.101048
Lopezosa CCodina LGuallar JPérez-Montoro M(2023)Voice search optimization in digital media: challenges, use and trainingEl Profesional de la información10.3145/epi.2023.may.07Online publication date: 9-May-2023
https://doi.org/10.3145/epi.2023.may.07
Melumad S(2023)Vocalizing Search: How Voice Technologies Alter Consumer Search Processes and SatisfactionJournal of Consumer Research10.1093/jcr/ucad00950:3(533-553)Online publication date: 9-Feb-2023
https://doi.org/10.1093/jcr/ucad009
Gülmez E(2022)Branded PodcastsHandbook of Research on the Future of Advertising and Brands in the New Entertainment Landscape10.4018/978-1-6684-3971-5.ch006(135-168)Online publication date: 14-Oct-2022
https://doi.org/10.4018/978-1-6684-3971-5.ch006
Stefaniak PStachowiak MKoperska WSkoczylas AŚliwiński P(2022)Application of Wearable Computer and ASR Technology in an Underground Mine to Support Mine Supervision of the Heavy Machinery ChamberSensors10.3390/s2219762822:19(7628)Online publication date: 8-Oct-2022
https://doi.org/10.3390/s22197628
Pricer L(2022)Two Essays Examining the Effects of AIVA Search on Cognition, Emotion and Choiceundefined10.12794/metadc1944351Online publication date: May-2022
https://doi.org/10.12794/metadc1944351
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Issue’s Table of Contents