# Towards Data-efficient Modeling for Wake Word Spotting

arXiv: https://arxiv.org/abs/2010.06659 · HTML: https://mlsanity.com/paper/2010.06659/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2020.

## Built on by (most cited first, 13 of 13)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Teaching keyword spotters to spot new keywords with limited examples](https://mlsanity.com/paper/2106.02443/) (2021)
- [Progressive Continual Learning for Spoken Keyword Spotting](https://mlsanity.com/paper/2201.12546/) (2022)
- [Enhancing Voice Wake-Up for Dysarthria: Mandarin Dysarthria Speech Corpus Release and Customized System Design](https://mlsanity.com/paper/2406.10304/) (2024)
- [Low-resource Low-footprint Wake-word Detection using Knowledge Distillation](https://mlsanity.com/paper/2207.03331/) (2022)
- [Wake-Cough: cough spotting and cougher identification for personalised long-term cough monitoring](https://mlsanity.com/paper/2110.03771/) (2021)
- [End-to-End Approach for Recognition of Historical Digit Strings](https://mlsanity.com/paper/2104.13666/) (2021)
- [Spot keywords from very noisy and mixed speech](https://mlsanity.com/paper/2305.17706/) (2023)
- [Towards noise robust trigger-word detection with contrastive learning pre-task for fast on-boarding of new trigger-words](https://mlsanity.com/paper/2111.03971/) (2021)
- [A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning](https://mlsanity.com/paper/2202.08509/) (2022)
- [Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space](https://mlsanity.com/paper/2409.12745/) (2024)
- [GraphemeAug: A Systematic Approach to Synthesized Hard Negative Keyword Spotting Examples](https://mlsanity.com/paper/2505.14814/) (2025)
- [Everything Counts: The Managed Omnirelevance of Speech in Human-Voice Agent Interaction](https://mlsanity.com/paper/2510.22610/) (2025)

## Abstract (arXiv)

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.