research-article

How Much Faster Can You Type by Speaking in Hindi?: Comparing Keyboard-Only and Keyboard+Speech Text Entry

Authors:

Anirudha Joshi,

Shashank Ahire,

Nimish MaraviAuthors Info & Claims

IndiaHCI '18: Proceedings of the 9th Indian Conference on Human-Computer Interaction

Pages 20 - 28

https://doi.org/10.1145/3297121.3297123

Published: 16 December 2018 Publication History

Abstract

Can a reasonably robust speech recognition engine improve text entry speeds in Indian languages in spite of the time spent by users in correcting errors? We investigate this question in this paper. We conducted a within-subject longitudinal study to evaluate performance of keyboard-only input and keyboard+speech input for Hindi with 20 novice users. We found that keyboard+speech input is 2.5 times faster than keyboard input. Results also showed that the difference in performance was lower for phrases picked from poems, songs and phrases that used less frequently used words. To the best of our knowledge, ours is the first study that compares performance of these two input modalities in an Indian language.

References

[1]

Anirudha Joshi, Girish Dalvi, Manjiri Joshi, Prasad Rashinkar, and Aniket Sarangdhar. 2011. Design and evaluation of Devanagari virtual keyboards for touch screen mobile phones. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI '11). ACM, New York, NY, USA, 323--332.

Digital Library

[2]

Girish Dalvi, Shashank Ahire, Nagraj Emmadi, Manjiri Joshi, Nirav Malsettar, Debasis Samanta, Devendra Jalihal, and Anirudha Joshi. 2015. A Protocol to Evaluate Virtual Keyboards for Indian Languages. In Proceedings of the 7th International Conference on HCI, IndiaHCI 2015 (IndiaHCI'15). ACM, New York, NY, USA, 27--38.

Digital Library

[3]

Girish Dalvi, Shashank Ahire, Nagraj Emmadi, Manjiri Joshi, Anirudha Joshi, Sanjay Ghosh, Prasad Ghone, and Narendra Parmar. 2016. Does prediction really help in Marathi text entry?: empirical analysis of a longitudinal study. In Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI '16). ACM, New York, NY, USA, 35--46.

Digital Library

[4]

Younghee Jung, Dhaval Joshi, Vijay Narayanan-Saroja, and Deepak Prabhu Desai. 2011. Solving the great Indian text entry puzzle: touch screen-based mobile text entry design. In Proceedings of the 13th International Conference on Human Computer Interaction with Mobile Devices and Services (MobileHCI '11). ACM, New York, NY, USA, 313--322.

Digital Library

[5]

Ben Shneiderman. 2000. The limits of speech recognition. Commun. ACM 43, 9 (September 2000), 63--65.

Digital Library

[6]

Prasenjit Dey, Ramchandrula Sitaram, Rahul Ajmera, and Kalika Bali. 2009. Voice key board: multimodal indic text entry. In Proceedings of the 2009 international conference on Multimodal interfaces (ICMI-MLMI '09). ACM, New York, NY, USA, 313--318.

Digital Library

[7]

Keith Vertanen and Per Ola Kristensson. 2009. Parakeet: a continuous speech recognition system for mobile touch-screen devices. In Proceedings of the 14th international conference on Intelligent user interfaces (IUI '09). ACM, New York, NY, USA, 237--246.

Digital Library

[8]

Sherry Ruan, Jacob O. Wobbrock, Kenny Liou, Andrew Ng, and James A. Landay. 2018. Comparing Speech and Keyboard Text Entry for Short Messages in Two Languages on Touchscreen Phones. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. 1, 4, Article 159 (January 2018), 23 pages.

Digital Library

[9]

Ankit Kuamr, Mohit Dua, and Arun Choudhary. 2014. Implementation and performance evaluation of continuous Hindi speech recognition. In Electronics and Communication Systems (ICECS), 2014 International Conference on. IEEE, 1--5).

[10]

Clare-Marie Karat, Christine Halverson, Daniel Horn, and John Karat. 1999. Patterns of entry and correction in large vocabulary continuous speech recognition systems. In Proceedings of the SIGCHI conference on Human Factors in Computing Systems (CHI '99). ACM, New York, NY, USA, 568--575.

Digital Library

[11]

Alexander I. Rudnicky, Michelle Sakamoto, and Joseph H. Polifroni. 1989. Evaluating spoken language interaction. In Proceedings of the workshop on Speech and Natural Language (HLT '89). Association for Computational Linguistics, Stroudsburg, PA, USA, 150--159.

Digital Library

[12]

Aditya Vashistha, Pooja Sethi, and Richard Anderson. 2017. Respeak: A Voice-based, Crowd-powered Speech Transcription System. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (CHI '17). ACM, New York, NY, USA, 1855--1866.

Digital Library

[13]

Kathleen J. Price, Min Lin, Jinjuan Feng, Rich Goldman, Andrew Sears, and Julie A. Jacko. 2004. Data Entry on the Move: An Examination of Nomadic Speech-Based Text Entry. Springer Berlin Heidelberg, Berlin, Heidelberg, 460--471.

[14]

Wikipedia. Devanagari. Retrieved July 06, 2018 from https://en.wikipedia.org/wiki/Devanagari

[15]

Liv.ai. 2018. Retrieved July 06, 2018 from https://liv.ai

[16]

Wikipedia. List of languages by number of native speakers in India. Retrieved on July 06, 2018 from https://en.wikipedia.org/wiki/List_of_languages_by_number_of_native_speakers_in_India

[17]

Wikipedia. List of languages by number of native speakers. Retrieved on July 06, 2018 from https://en.wikipedia.org/wiki/List_of_languages_by_number_of_native_speakers

[18]

R. William Soukoreff and I. Scott MacKenzie. 2004. Recent developments in text-entry error rate measurement. In CHI '04 Extended Abstracts on Human Factors in Computing Systems (CHI EA '04). ACM, New York, NY, USA, 1425--1428.

Digital Library

[19]

CDAC InScript: Unified Virtual Keyboard for Indian Languages, 2018. http://www.cdac.in/index.aspx?id=dl_android_uvkil

[20]

SwiftKey Keyboard, 2018. https://play.google.com/store/apps/details?id=com.touchtype.swiftkey&hl=en

[21]

Swarachakra Marathi Keyboard, 2018. https://play.google.com/store/apps/details?id=iit.android.swarachakraMarathi

[22]

Sparsh Marathi keyboard, 2018. https://play.google.com/store/apps/details?id=com.sparsh.inputmethod.marathi&hl=en

[23]

Wikipedia. Abugida. Retrieved on September 28, 2018 from https://en.wikipedia.org/wiki/Abugida

[24]

David A Rosenbaum. 2009. Human motor control. Academic press.

[25]

Sherry Ruan, Jacob O.Wobbrock, Kenny Liou, Andrew Ng, James Landay.2016.Speech Is 3x Faster than Typing for English and Mandarin Text Entry on Mobile Devices

Cited By

Lottridge DYoon CBurton DWang CKaye J(2022)Ally: Understanding Text Messaging to Build a Better Onscreen Keyboard for Blind PeopleACM Transactions on Accessible Computing10.1145/353370715:4(1-22)Online publication date: 22-Oct-2022
https://dl.acm.org/doi/10.1145/3533707
Hayat SRextin ABilal A(2021)Understanding the usability issues in contact management of illiterate and semi-literate usersPLOS ONE10.1371/journal.pone.025971916:12(e0259719)Online publication date: 2-Dec-2021
https://doi.org/10.1371/journal.pone.0259719
Jaber RMcMillan DTorres MSchlögl SClark LPorcheron M(2020)Conversational User Interfaces on Mobile DevicesProceedings of the 2nd Conference on Conversational User Interfaces10.1145/3405755.3406130(1-11)Online publication date: 22-Jul-2020
https://dl.acm.org/doi/10.1145/3405755.3406130
Show More Cited By

Index Terms

How Much Faster Can You Type by Speaking in Hindi?: Comparing Keyboard-Only and Keyboard+Speech Text Entry
1. Human-centered computing
  1. Human computer interaction (HCI)

Recommendations

Automatic speech segmentation in syllable centric speech recognition system

Speech recognition is the process of understanding the human or natural language speech by a computer. A syllable centric speech recognition system in this aspect identifies the syllable boundaries in the input speech and converts it into the respective ...
A waveform concatenation technique for text-to-speech synthesis

Designing text-to-speech systems capable of producing natural sounding speech segments in different Indian languages is a challenging and ongoing problem. Due to the large number of possible pronunciations in different Indian languages, a number of ...
Approaches for Multilingual Phone Recognition in Code-switched and Non-code-switched Scenarios Using Indian Languages
In this study, we evaluate and compare two different approaches for multilingual phone recognition in code-switched and non-code-switched scenarios. First approach is a front-end Language Identification (LID)-switched to a monolingual phone recognizer (...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

IndiaHCI '18: Proceedings of the 9th Indian Conference on Human-Computer Interaction

December 2018

134 pages

ISBN:9781450362146

DOI:10.1145/3297121

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]

In-Cooperation

South India ACM SIGCHI: The South India ACM SIGCHI Chapter

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 December 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

IndiaHCI'18

IndiaHCI'18: IndiaHCI 2018

December 16 - 18, 2018

Bangalore, India

Acceptance Rates

IndiaHCI '18 Paper Acceptance Rate 16 of 38 submissions, 42%;

Overall Acceptance Rate 33 of 93 submissions, 35%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
197
Total Downloads

Downloads (Last 12 months)50
Downloads (Last 6 weeks)1

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Lottridge DYoon CBurton DWang CKaye J(2022)Ally: Understanding Text Messaging to Build a Better Onscreen Keyboard for Blind PeopleACM Transactions on Accessible Computing10.1145/353370715:4(1-22)Online publication date: 22-Oct-2022
https://dl.acm.org/doi/10.1145/3533707
Hayat SRextin ABilal A(2021)Understanding the usability issues in contact management of illiterate and semi-literate usersPLOS ONE10.1371/journal.pone.025971916:12(e0259719)Online publication date: 2-Dec-2021
https://doi.org/10.1371/journal.pone.0259719
Jaber RMcMillan DTorres MSchlögl SClark LPorcheron M(2020)Conversational User Interfaces on Mobile DevicesProceedings of the 2nd Conference on Conversational User Interfaces10.1145/3405755.3406130(1-11)Online publication date: 22-Jul-2020
https://dl.acm.org/doi/10.1145/3405755.3406130
Hong JVaing CKacorri HFindlater L(2020)Reviewing Speech Input with AudioACM Transactions on Accessible Computing10.1145/338203913:1(1-28)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3382039
Bhikne BJoshi AJoshi MJadhav CSakhardande P(2019)Faster and Less Error-Prone: Supplementing an Accessible Keyboard with Speech InputHuman-Computer Interaction – INTERACT 201910.1007/978-3-030-29381-9_18(288-304)Online publication date: 23-Aug-2019
https://doi.org/10.1007/978-3-030-29381-9_18

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 Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten