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，可通过语音音频中同时说话者数量进行度量。该度量已支持多种心理健康追踪和人机物三元融合物联网应用。尽管设备端社交氛围度量（SAM）对保障用户隐私、促进上述应用广泛部署至关重要，但现有基于深度神经网络（DNN）的SAM解决方案所需计算复杂度与移动设备资源受限性存在矛盾。此外，由于临床场景下的隐私限制和人力需求，可获取或实际的带标签数据极为有限，进一步挑战了设备端SAM方案的准确率。为此，我们提出面向能效与实时社交氛围度量的专用神经架构搜索框架ERSAM。具体而言，ERSAM框架可自动搜索能推动移动端SAM方案在准确率与硬件效率平衡前沿的DNN模型。例如，在Pixel 3手机上处理5秒音频片段时，ERSAM生成的DNN仅消耗40 mW×12小时能耗和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化设备端SAM解决方案铺平道路。