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.

通过增强数据的时间对齐性和使用基于两个分辨率阶段的检测，本研究提出了一种声控界面的优化方案，包括轻量级即时处理音频流的设备端模型和云端验证模型。在隐私保护方面，该方案将音频特征发送到云端而非原始音频，且在各噪声条件下，所提出的模型优于更强的分类器。