How Much Faster Can You Type by Speaking in Hindi?

Bhikne, Bhakti; Joshi, Anirudha; Joshi, Manjiri; Ahire, Shashank; Maravi, Nimish

doi:10.1145/3297121.3297123

Cited by 5 publications

(17 citation statements)

References 14 publications

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“…As mentioned above, the much slower text entry rates have been reported for Indian languages, and speech seems to help particularly in these languages. After a between-subject longitudinal study with novice users lasting several weeks and providing about 300 minutes of controlled typing practice, we reported peak speeds between 35 to 45 CPM on four Marathi keyboards, namely Swarachakra Marathi, CDAC InScript Devanagari, Swiftkey Marathi and Sparsh Marathi [8]. In our more recent study, we reported substantial gains by adding speech input to the Swarachakra Hindi keyboard [8].…”

Section: Introductionmentioning

confidence: 83%

“…The only study for text input by visually impaired users for an Indian language reported 15 CPM using the Swarachakra keyboard and 13 CPM using the Google Indic keyboard [7]. In our recent study, we found that enhancing the keyboard with speech input could enable sighted users achieve mean speeds of 118 CPM in Hindi [8]. In this paper, we investigate if we could we achieve similar improvements in performance for visually impaired users.…”

Section: Introductionmentioning

confidence: 94%

“…After a between-subject longitudinal study with novice users lasting several weeks and providing about 300 minutes of controlled typing practice, we reported peak speeds between 35 to 45 CPM on four Marathi keyboards, namely Swarachakra Marathi, CDAC InScript Devanagari, Swiftkey Marathi and Sparsh Marathi [8]. In our more recent study, we reported substantial gains by adding speech input to the Swarachakra Hindi keyboard [8]. We found that novice users could achieve mean speeds of up to 118 CPM with speech, compared to 47 CPM without speech.…”

Section: Introductionmentioning

confidence: 99%

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Faster and Less Error-Prone: Supplementing an Accessible Keyboard with Speech Input

Bhikne

Joshi

et al. 2019

Human-Computer Interaction – INTERACT 2019

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Swarachakra is an Abugida text input keyboard available in 12 Indian languages. We enhanced an accessible version of Swarachakra Marathi with speech input. However, speech input could be error-prone, and especially so for languages where speech recognition technologies are new. Such errors could either slow the user down due to the need for editing, or go unnoticed, leading to high uncorrected error rates. We therefore conducted a withinsubject empirical study to compare the user performance of keyboard-only input method with keyboard+speech input method with 11 novice visually impaired users. We found that keyboard+speech input was almost 11 times faster, reaching 182 characters per minute, and had a lower uncorrected error rate than the keyboard-only input, and in spite of having higher corrected error rates. Though we used a wide variety of phrases in our study, we observed that all phrases were faster on average with the keyboard+speech input method. To the best of our knowledge, ours is the first empirical study to evaluate the performance of speech enabled text input in Marathi for visually impaired people. This is the highest reported speed by visually impaired users in any Indian language.

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Section: Introductionmentioning

confidence: 83%

Section: Introductionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Faster and Less Error-Prone: Supplementing an Accessible Keyboard with Speech Input

Bhikne

Joshi

et al. 2019

Human-Computer Interaction – INTERACT 2019

Self Cite

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“…Text entry [9,38,67,71,82,85,93] Application control [22,90,92] Speech analysis [26,36,52,68,72] Spoken output [28,31,66,77] Conversational Agents [33,43,45,48,55,61,96] Probes [18,74] Domains Education [22,43,52,61,68,77] Health & Well-being [26,28,36,71,72,92,94] Transportation [33,38,55,67,90] Gaming [18,26,36,52,66,…”

Section: References Themesmentioning

confidence: 99%

“…This gives evidence that speech-based technologies can reduce manual and visual interference when compared to using touch screen in driving context. A comparison study conducted by Bhikne et al [9] found that the combination of speech input with a keyboard for error correction was more than 2.5 times faster than using only the keyboard. Ruan et al [82] argue that speechbased text entry in English could be almost 3x times faster than keyboards.…”

Section: Text Entry and Typingmentioning

confidence: 99%

Conversational User Interfaces on Mobile Devices

Jaber

2020

Proceedings of the 2nd Conference on Conversational User Interfaces

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Conversational User Interfaces (CUI) on mobile devices are the most accessible and widespread examples of voice-based interaction in the wild. This paper presents a survey of mobile conversation user interface research since the commercial deployment of Apple's Siri, the first readily available consumer CUI. We present and discuss Text Entry & Typing, Application Control, Speech Analysis, Conversational Agents, Spoken Output, & Probes as the prevalent themes of research in this area. We also discuss this body of work in relation to the domains of Health & Well-being, Education, Games, and Transportation. We conclude this paper with a discussion on Multi-modal CUIs, Conversational Repair, and the implications for CUIs of greater access to the context of use. CCS CONCEPTS• Computing methodologies → Speech recognition; • Humancentered computing → Interaction techniques; Smartphones; • General and reference → Surveys and overviews.

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Reviewing Speech Input with Audio

Hong

Vaing

Kacorri

et al. 2020

ACM Trans. Access. Comput.

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Speech input is a primary method of interaction for blind mobile device users, yet the process of dictating and reviewing recognized text through audio only (i.e., without access to visual feedback) has received little attention. A recent study found that sighted users could identify only about half of automatic speech recognition (ASR) errors when listening to text-to-speech output of the ASR results. Blind screen reader users, in contrast, may be better able to identify ASR errors through audio due to their greater use of speech interaction and increased ability to comprehend synthesized speech. To compare the experiences of blind and sighted users with speech input and ASR errors, as well as to compare their ability to identify ASR errors through audio-only interaction, we conducted a lab study with 12 blind and 12 sighted participants. The study included a semi-structured interview portion to qualitatively understand experiences with ASR, followed by a controlled speech input task to quantitatively compare participants’ ability to identify ASR errors in their dictated text. Findings revealed differences between blind and sighted participants in terms of how they use speech input and their level of concern for ASR errors (e.g., blind users were more highly concerned). In the speech input task, blind participants were able to identify only 40% of ASR errors, which, counter to our hypothesis, was not significantly different from sighted participants’ performance. In depth analysis of speech input, ASR errors, and strategy of identifying ASR errors scrutinized how participants entered a text with speech input and reviewed it. Our findings indicate the need for future work on how to support blind users in confidently using speech input to generate accurate, error-free text.

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How Much Faster Can You Type by Speaking in Hindi?

Cited by 5 publications

References 14 publications

Faster and Less Error-Prone: Supplementing an Accessible Keyboard with Speech Input

Faster and Less Error-Prone: Supplementing an Accessible Keyboard with Speech Input

Conversational User Interfaces on Mobile Devices

Reviewing Speech Input with Audio

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