# Efficient keyword spotting using time delay neural networks

arXiv: https://arxiv.org/abs/1807.04353 · HTML: https://mlsanity.com/paper/1807.04353/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2018.

## Built on by (most cited first, 5 of 5)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Neural Model Reprogramming with Similarity Based Mapping for Low-Resource Spoken Command Recognition](https://mlsanity.com/paper/2110.03894/) (2021)
- [Speech Understanding on Tiny Devices with A Learning Cache](https://mlsanity.com/paper/2311.18188/) (2023)
- [To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement](https://mlsanity.com/paper/2304.03416/) (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)

## Abstract (arXiv)

This paper describes a novel method of live keyword spotting using a two-stage time delay neural network. The model is trained using transfer learning: initial training with phone targets from a large speech corpus is followed by training with keyword targets from a smaller data set. The accuracy of the system is evaluated on two separate tasks. The first is the freely available Google Speech Commands dataset. The second is an in-house task specifically developed for keyword spotting. The results show significant improvements in false accept and false reject rates in both clean and noisy environments when compared with previously known techniques. Furthermore, we investigate various techniques to reduce computation in terms of multiplications per second of audio. Compared to recently published work, the proposed system provides up to 89% savings on computational complexity.