As the French, European and worldwide populations are aging, there is a strong interest for new systems that guarantee a reliable and privacy preserving home monitoring for frailty prevention. This work is a part of a global environmental audio analysis system which aims to help identification of Activities of Daily Life (ADL) through human and everyday life sounds recognition, speech presence and number of speakers detection. The focus is made on the number of speakers detection. In this article, we present how recent advances in sound processing and speaker diarization can improve the existing embedded systems. We study the performances of two new methods and discuss the benefits of DNN based approaches which improve performances by about 100%.

翻译：随着法国、欧洲及全球人口老龄化，人们对开发可靠且保护隐私的居家监测系统以预防衰弱表现出强烈兴趣。本研究隶属于一个全局环境音频分析系统，旨在通过人类及日常生活声音识别、语音存在检测及说话人数检测辅助日常活动识别。重点聚焦于说话人数检测。本文阐述了声音处理与说话人日志分析领域的最新进展如何改进现有嵌入式系统。我们研究了两种新方法的性能表现，并讨论了基于深度神经网络的方法带来的效益——该方法将检测性能提升了约100%。