Wake word (WW) spotting is challenging in far-field because of not only the interference in signal transmission but also the complexity in acoustic environment. Traditional WW model training requires a large amount of in-domain WW-specific data with substantial human annotations. This prevents the model building in the situation of lacking 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 stratified data augmentation, which improves the model robustness to a variety of predefined acoustic conditions, together with a semi-supervised learning pipeline to extract the WW and adversarial examples from an 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 complexity in acoustic environments. 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 to collect and 86% of the bandwidth for annotation.
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