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Improving command and control speech recognition on mobile devices: using predictive user models for language modeling

机译:改善移动设备上的命令和控制语音识别:使用预测性用户模型进行语言建模

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摘要

Command and control (C&C) speech recognition allows users to interact with a system by speaking commands or asking questions restricted to a fixed grammar containing pre-defined phrases. Whereas C&C interaction has been commonplace in telephony and accessibility systems for many years, only recently have mobile devices had the memory and processing capacity to support client-side speech recognition. Given the personal nature of mobile devices, statistical models that can predict commands based in part on past user behavior hold promise for improving C&C recognition accuracy. For example, if a user calls a spouse at the end of every workday, the language model could be adapted to weight the spouse more than other contacts during that time. In this paper, we describe and assess statistical models learned from a large population of users for predicting the next user command of a commercial C&C application. We explain how these models were used for language modeling, and evaluate their performance in terms of task completion. The best performing model achieved a 26% relative reduction in error rate compared to the base system. Finally, we investigate the effects of personalization on performance at different learning rates via online updating of model parameters based on individual user data. Personalization significantly increased relative reduction in error rate by an additional 5%.
机译:命令与控制(C&C)语音识别允许用户通过说出命令或询问限于包含预定义短语的固定语法的问题来与系统交互。尽管C&C交互在电话和可访问性系统中已经很常见了很多年,但是直到最近,移动设备才具有支持客户端语音识别的存储和处理能力。考虑到移动设备的个人性质,可以部分基于过去的用户行为来预测命令的统计模型有望改善C&C识别的准确性。例如,如果用户在每个工作日结束时给配偶打电话,则该语言模型可适合于在这段时间内给配偶施加比其他联系人更大的权重。在本文中,我们描述并评估了从大量用户那里学到的统计模型,用于预测商业C&C应用程序的下一个用户命令。我们将说明如何将这些模型用于语言建模,并根据任务完成情况评估其性能。与基本系统相比,性能最佳的模型的错误率相对降低了26%。最后,我们通过基于个人用户数据的模型参数在线更新来研究个性化对不同学习率下的性能的影响。个性化可以显着提高错误率的相对降低,额外降低5%。

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