  Voice-based interfaces rely on a wake-up word mechanism to initiate
communication with devices. However, achieving a robust, energy-efficient, and
fast detection remains a challenge. This paper addresses these real production
needs by enhancing data with temporal alignments and using detection based on
two phases with multi-resolution. It employs two models: a lightweight
on-device model for real-time processing of the audio stream and a verification
model on the server-side, which is an ensemble of heterogeneous architectures
that refine detection. This scheme allows the optimization of two operating
points. To protect privacy, audio features are sent to the cloud instead of raw
audio. The study investigated different parametric configurations for feature
extraction to select one for on-device detection and another for the
verification model. Furthermore, thirteen different audio classifiers were
compared in terms of performance and inference time. The proposed ensemble
outperforms our stronger classifier in every noise condition.
