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Speech recognition in a smart home: Some experiments for telemonitoring

机译：在智能家居中的语音识别：远程看法的一些实验

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Because of the aging of the population, low-cost solutions are required to help people with loss of autonomy staying at home rather than in public health centers. One solution is to assist human operators with smart information systems. In this case, position and physiologic sensors already give important information, but there are few studies about the utility of sound in patient's habitation. However, sound classification and speech recognition may greatly increase the versatility of such a system: this will be provided by detecting short sentences or words that could characterize a distress situation for the patient. Moreover, analysis and classification of sounds emitted in patient's habitation may be useful for patient's activity monitoring. In this paper, we present a global speech and sound recognition system that can be set-up in a flat. Eight microphones were placed in the health smart home of Grenoble (named HIS, a real living flat of 47m2) to automatically analyze and classify different sounds and speech utterances (e.g.: normal or distress French sentences). Sounds are clustered in eight classes but this aspect is not discussed in this paper. For speech signals, an input utterance is recognized and a subsequent process classifies it in normal or distress, by analysing the presence of distress keywords. An experimental protocol was defined and then this system has been evaluated in uncontrolled conditions in which heterogeneous speakers were asked to utter predetermined sentences in the HIS. The results of this experiment, where ten subjects were involved, are presented. The global error rate was 15.6%. Moreover, noise suppression techniques were incorporated in the speech and sound recognition system in order to suppress the noise emitted by known sources like TV or radio. An experimental protocol was defined and tested by four speakers in real conditions inside a room. Finally, we discuss the results of this experiment as a function of the noise sour- ce: speech or music.
机译：由于人口老化，需要低成本的解决方案来帮助人们丧失自主留在家里而不是公共卫生中心。一个解决方案是帮助人类运营商与智能信息系统。在这种情况下，位置和生理传感器已经提供了重要信息，但是患者居住地的声音效用很少有研究。然而，声音分类和语音识别可能会大大增加这种系统的多功能性：这将通过检测可以表征患者遇到痛苦情况的短句或单词来提供。此外，患者居住地发出的​​声音的分析和分类对于患者的活动监测可能是有用的。在本文中，我们展示了一个全球语音和声音识别系统，可以在一个公寓中设置。将八个麦克风置于Grenoble的健康智能家中（命名为47平方米），自动分析和分类不同的声音和语音话语（例如：正常或遇险法国句子）。声音在八个类中聚集，但本文没有讨论此方面。对于语音信号，通过分析遇险关键字的存在，识别出输入话语，随后的过程将其分类为正常或遇险。定义了实验方案，然后在不受控制的条件下评估了该系统，其中不均匀的扬声器在他的情况下完全句子。介绍了该实验的结果，其中涉及十个受试者。全球错误率为15.6％。此外，在语音和声音识别系统中结合了噪声抑制技术，以抑制所知来源像电视或无线电一样的噪声。在房间内的真实条件下由四个发言机定义和测试实验方案。最后，我们将该实验的结果作为噪声措施：语音或音乐的函数。

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