# Sequence-to-sequence Models for Small-Footprint Keyword Spotting

arXiv: https://arxiv.org/abs/1811.00348 · HTML: https://mlsanity.com/paper/1811.00348/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2018.

## Built on by (most cited first, 2 of 2)

- [Visual Keyword Spotting with Attention](https://mlsanity.com/paper/2110.15957/) (2021)
- [Text Anchor Based Metric Learning for Small-footprint Keyword Spotting](https://mlsanity.com/paper/2108.05516/) (2021)

## Abstract (arXiv)

In this paper, we propose a sequence-to-sequence model for keyword spotting (KWS). Compared with other end-to-end architectures for KWS, our model simplifies the pipelines of production-quality KWS system and satisfies the requirement of high accuracy, low-latency, and small-footprint. We also evaluate the performances of different encoder architectures, which include LSTM and GRU. Experiments on the real-world wake-up data show that our approach outperforms the recently proposed attention-based end-to-end model. Specifically speaking, with 73K parameters, our sequence-to-sequence model achieves ~3.05% false rejection rate (FRR) at 0.1 false alarm (FA) per hour.