Wake word (WW) spotting is challenging in far-field not only because of the
interference in signal transmission but also the complexity in acoustic
environments. Traditional WW model training requires large amount of in-domain
WW-specific data with substantial human annotations therefore it is hard to
build WW models without such data. In this paper we present data-efficient
solutions to address the challenges in WW modeling, such as domain-mismatch,
noisy conditions, limited annotation, etc. Our proposed system is composed of a
multi-condition training pipeline with a stratified data augmentation, which
improves the model robustness to a variety of predefined acoustic conditions,
together with a semi-supervised learning pipeline to accurately extract the WW
and confusable examples from untranscribed speech corpus. Starting from only 10
hours of domain-mismatched WW audio, we are able to enlarge and enrich the
training dataset by 20-100 times to capture the acoustic complexity. Our
experiments on real user data show that the proposed solutions can achieve
comparable performance of a production-grade model by saving 97\% of the amount
of WW-specific data collection and 86\% of the bandwidth for annotation.

本研究提供了一种数据高效的跨领域唤醒词模型训练技术，该技术包括多种声学环境的训练管道和半监督学习管道，可以用 10 个小时的领域不匹配的音频来增强模型鲁棒性，并从未经译写的语音语料库中准确提取 WW 和可能相似的例子，所提出的解决方案可节省 97％的具体 WW 数据收集和 86％的注释带宽并达到产业级模型的可比性能。