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Speak-As-You-Swipe (SAYS): A Multimodal Interface Combining Speech and Gesture Keyboard Synchronously for Continuous Mobile Text Entry

机译:讲话 - 您 - 刷新(说):多模型接口与连续移动文本条目同步组合语音和手势键盘

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Modern mobile devices, such as the smartphones and tablets, are becoming increasingly popular amongst users of all ages. Text entry is one of the most important modes of interaction between human and their mobile devices. Although typing on a touchscreen display using a soft keyboard remains the most common text input method for many users, the process can be frustratingly slow, especially on smartphones with a much smaller screen. Voice input offers an attractive alternative that completely eliminates the need for typing. However, voice input relies on automatic speech recognition technology whose performance degrades significantly in noisy environment or for non-native users. This paper presents Speak-As-You-Swipe (SAYS), a novel multimodal interface that enables efficient continuous text entry on mobile devices. SAYS integrates a gesture keyboard with speech recognition to improve the efficiency and accuracy of text entry. The swipe gesture and voice inputs provide complementary information that can be very effective in disambiguating confusions in word predictions. The word prediction hypotheses from a gesture keyboard are directly incorporated into the speech recognition process so that the SAYS interface can handle continuous input. Experimental results show that for a 20k vocabulary, the proposed SAYS interface can achieve prediction accuracy of 96.4% in clean condition and about 94.0% in noisy environment, compared to 92.2% using a gesture keyboard alone.
机译:现代移动设备,如智能手机和平板电脑,在所有年龄段的用户中都变得越来越受欢迎。文本输入是人与其移动设备之间最重要的互动模式之一。虽然使用软键盘在触摸屏显示器上键入仍然是许多用户最常见的文本输入方法,但该过程可能会令人沮丧,特别是在具有更小屏幕的智能手机上。语音输入提供了一种有吸引力的替代方案,可以完全消除键入的需要。但是,语音输入依赖于自动语音识别技术,其性能在嘈杂的环境中显着降低或用于非本机用户。本文介绍了您的讲话 - 你刷新(说),这是一种新型多模式接口,可以在移动设备上实现有效的连续文本输入。说与语音识别集成了一个手势键盘,以提高文本输入的效率和准确性。滑动手势和语音输入提供互补信息,可以非常有效地消除Word预测中的混淆。从手势键盘的单词预测假设直接结合到语音识别过程中,使得所述接口可以处理连续输入。实验结果表明,对于20K词汇,提出的界面可以在清洁条件下达到96.4%的预测精度,嘈杂的环境中约为94.0%,而单独使用手势键盘的92.2%。

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