Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches
Abstract
Wake word detection exists in most intelligent homes and portable devices. It offers these devices the ability to "wake up" when summoned at a low cost of power and computing. This paper focuses on understanding alignment's role in developing a wake-word system that answers a generic phrase. We discuss three approaches. The first is alignment-based, where the model is trained with frame-wise cross-entropy. The second is alignment-free, where the model is trained with CTC. The third, proposed by us, is a hybrid solution in which the model is trained with a small set of aligned data and then tuned with a sizeable unaligned dataset. We compare the three approaches and evaluate the impact of the different aligned-to-unaligned ratios for hybrid training. Our results show that the alignment-free system performs better than the alignment-based for the target operating point, and with a small fraction of the data (20%), we can train a model that complies with our initial constraints.
Cite
@article{arxiv.2302.08950,
title = {Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches},
author = {Vinicius Ribeiro and Yiteng Huang and Yuan Shangguan and Zhaojun Yang and Li Wan and Ming Sun},
journal= {arXiv preprint arXiv:2302.08950},
year = {2023}
}
Comments
Accepted to Interspeech 2023