As voice-controlled IoT proliferate, processing everything in the cloud is too costly, privacy-invasive, and slow. The future belongs to hybrid architectures where the edge handles the initial 'wake-up' and the cloud handles the heavy lifting.
Build an ultra-lightweight, highly accurate keyword spotting (KWS) model that runs locally on a low-power device.
As voice-controlled IoT proliferate, processing everything in the cloud is too costly, privacy-invasive, and slow
Build an ultra-lightweight, highly accurate keyword spotting (KWS) model that runs locally on a low-power device. Upon detecting the keyword, the system must instantly and efficiently stream the subsequent audio to a remote Automated Speech Recognition (ASR) server with minimal data overhead and latency. Key Metrics for Evaluation • Efficiency: Model size (RAM/Flash footprint) and CPU usage during idle listening. • Accuracy: High true-positive rate for the keyword with near-zero false activations. • Latency: The time delta between the keyword ending and the cloud ASR receiving the audio stream. Software & Framework Restrictions • Open-Source Only: The use of proprietary, closed-source, or commercial voice-activation SDKs is strictly prohibited. • Allowed Frameworks: Teams must build their