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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:滑动即说(SAYS):将语音和手势键盘同步结合以实现连续移动文本输入的多模式界面

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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,例如智能手机和平板电脑,在所有年龄段的用户中都越来越受欢迎。文本输入是人类与其移动设备之间最重要的交互方式之一。尽管对于许多用户而言,使用软键盘在触摸屏显示器上打字仍然是最常见的文本输入方法,但该过程可能令人沮丧地缓慢,尤其是在屏幕小得多的智能手机上。语音输入提供了一种有吸引力的替代方法,它完全不需要键入。但是,语音输入依赖于自动语音识别技术,该技术在嘈杂的环境中或对于非本地用户的性能都会大大降低。本文介绍了随笔即说(SAYS),这是一种新颖的多模式界面,可在移动设备上进行高效的连续文本输入。 SAYS将手势键盘与语音识别集成在一起,以提高文本输入的效率和准确性。滑动手势和语音输入可提供补充信息,这些信息可以非常有效地消除单词预测中的混淆。来自手势键盘的单词预测假设直接合并到语音识别过程中,以便SAYS接口可以处理连续输入。实验结果表明,对于20k的词汇量,所提出的SAYS界面在干净的条件下可达到96.4%的预测准确度,在嘈杂的环境中可达到94.0%的预测准确度,而仅使用手势键盘可达到92.2%的预测准确度。

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