Social ambiance describes the context in which social interactions happen, and can be measured using speech audio by counting the number of concurrent speakers. This measurement has enabled various mental health tracking and human-centric IoT applications. While on-device Socal Ambiance Measure (SAM) is highly desirable to ensure user privacy and thus facilitate wide adoption of the aforementioned applications, the required computational complexity of state-of-the-art deep neural networks (DNNs) powered SAM solutions stands at odds with the often constrained resources on mobile devices. Furthermore, only limited labeled data is available or practical when it comes to SAM under clinical settings due to various privacy constraints and the required human effort, further challenging the achievable accuracy of on-device SAM solutions. To this end, we propose a dedicated neural architecture search framework for Energy-efficient and Real-time SAM (ERSAM). Specifically, our ERSAM framework can automatically search for DNNs that push forward the achievable accuracy vs. hardware efficiency frontier of mobile SAM solutions. For example, ERSAM-delivered DNNs only consume 40 mW x 12 h energy and 0.05 seconds processing latency for a 5 seconds audio segment on a Pixel 3 phone, while only achieving an error rate of 14.3% on a social ambiance dataset generated by LibriSpeech. We can expect that our ERSAM framework can pave the way for ubiquitous on-device SAM solutions which are in growing demand.

翻译：社交氛围描述了社交互动发生的环境，可通过语音音频中并发说话者数量进行测量。该测量已推动多种心理健康追踪及以人为中心的物联网应用发展。尽管设备端社交氛围测量（SAM）对保障用户隐私、促进上述应用广泛应用至关重要，但当前基于深度神经网络（DNN）的SAM解决方案所需计算复杂度与移动设备有限的资源相矛盾。此外，在临床场景下，受隐私限制与人工标注成本制约，可用或实用的标注数据极为有限，这进一步挑战了设备端SAM解决方案的精度。为此，我们提出面向高能效实时SAM的专用神经架构搜索框架ERSAM。具体而言，ERSAM框架可自动搜索能突破移动SAM解决方案精度与硬件效率权衡前沿的DNN。例如，在Pixel 3手机上处理5秒音频段时，ERSAM生成的DNN仅消耗40毫瓦×12小时的能量与0.05秒处理延迟，在基于LibriSpeech生成的社交氛围数据集上错误率仅为14.3%。可以预见，ERSAM框架将为需求日益增长的普适设备端SAM解决方案铺平道路。