基于语音和触摸的智能手机容错多模态文本编辑和校正。

Maozheng Zhao, Wenzhe Cui, I V Ramakrishnan, Shumin Zhai, Xiaojun Bi
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引用次数: 5

摘要

在智能手机上进行编辑操作,如剪切、复制、粘贴和纠正输入文本中的错误,通常是乏味且具有挑战性的。本文提出了一种基于语音和触摸的智能手机多模态文本编辑和纠错方法。要使用VT编辑文本,用户可以用手指在文本片段上滑动并发出命令,例如“加粗”来更改片段的格式,或者用户可以在文本区域内点击并发出命令,例如“突出显示此段落”来编辑文本。对于文本更正,用户在错误文本片段的区域附近点击,并指示替换或插入的新内容。VT将触摸和语音输入与语言模型和短语相似度等语言语境相结合,推断用户的编辑意图,可以处理歧义和噪声输入信号。与现有的纠错方法(如iOS的语音控制)相比,这是一个很大的优势,因为现有的纠错方法需要精确的光标控制或文本选择。我们的评估表明,与纯触控和iOS的语音控制相比,VT显著提高了智能手机上文本编辑和文本校正的效率。我们的用户研究表明,与纯触控相比,VT将文本编辑时间减少了30.80%,文本更正时间减少了29.97%。与iOS的Voice Control方式相比,VT将文本编辑时间减少了30.81%,文本纠错时间减少了47.96%。
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Voice and Touch Based Error-tolerant Multimodal Text Editing and Correction for Smartphones.

Editing operations such as cut, copy, paste, and correcting errors in typed text are often tedious and challenging to perform on smartphones. In this paper, we present VT, a voice and touch-based multi-modal text editing and correction method for smartphones. To edit text with VT, the user glides over a text fragment with a finger and dictates a command, such as "bold" to change the format of the fragment, or the user can tap inside a text area and speak a command such as "highlight this paragraph" to edit the text. For text correcting, the user taps approximately at the area of erroneous text fragment and dictates the new content for substitution or insertion. VT combines touch and voice inputs with language context such as language model and phrase similarity to infer a user's editing intention, which can handle ambiguities and noisy input signals. It is a great advantage over the existing error correction methods (e.g., iOS's Voice Control) which require precise cursor control or text selection. Our evaluation shows that VT significantly improves the efficiency of text editing and text correcting on smartphones over the touch-only method and the iOS's Voice Control method. Our user studies showed that VT reduced the text editing time by 30.80%, and text correcting time by 29.97% over the touch-only method. VT reduced the text editing time by 30.81%, and text correcting time by 47.96% over the iOS's Voice Control method.

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Accessible Gesture Typing on Smartphones for People with Low Vision. Voice and Touch Based Error-tolerant Multimodal Text Editing and Correction for Smartphones. Modeling Touch Point Distribution with Rotational Dual Gaussian Model. UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology, Virtual Event, USA, October 10-14, 2021 UIST '21: The Adjunct Publication of the 34th Annual ACM Symposium on User Interface Software and Technology, Virtual Event, USA, October 10-14, 2021
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