# Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution

arXiv: https://arxiv.org/abs/2010.09960 · HTML: https://mlsanity.com/paper/2010.09960/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Oct 2020.

## Built on by (most cited first, 15 of 15)

- [Broadcasted Residual Learning for Efficient Keyword Spotting](https://mlsanity.com/paper/2106.04140/) (2021)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [A Streaming End-to-End Framework For Spoken Language Understanding](https://mlsanity.com/paper/2105.10042/) (2021)
- [LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting](https://mlsanity.com/paper/2211.04635/) (2022)
- [Text Anchor Based Metric Learning for Small-footprint Keyword Spotting](https://mlsanity.com/paper/2108.05516/) (2021)
- [End-to-End Efficiency in Keyword Spotting: A System-Level Approach for Embedded Microcontrollers](https://mlsanity.com/paper/2509.07051/) (2025)
- [Sub 8-Bit Quantization of Streaming Keyword Spotting Models for Embedded Chipsets](https://mlsanity.com/paper/2207.06920/) (2022)
- [To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement](https://mlsanity.com/paper/2304.03416/) (2023)
- [MONAQ: Multi-Objective Neural Architecture Querying for Time-Series Analysis on Resource-Constrained Devices](https://mlsanity.com/paper/2505.10607/) (2025)
- [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)
- [RepCNN: Micro-sized, Mighty Models for Wakeword Detection](https://mlsanity.com/paper/2406.02652/) (2024)
- [PatchDSU: Uncertainty Modeling for Out of Distribution Generalization in Keyword Spotting](https://mlsanity.com/paper/2508.03190/) (2025)
- [Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices](https://mlsanity.com/paper/2508.04857/) (2025)
- [Transformer Redesign for Late Fusion of Audio-Text Features on Ultra-Low-Power Edge Hardware](https://mlsanity.com/paper/2510.18036/) (2025)
- [PCOV-KWS: Multi-task Learning for Personalized Customizable Open Vocabulary Keyword Spotting](https://mlsanity.com/paper/2603.18023/) (2026)

## Abstract (arXiv)

Keyword Spotting (KWS) plays a vital role in human-computer interaction for smart on-device terminals and service robots. It remains challenging to achieve the trade-off between small footprint and high accuracy for KWS task. In this paper, we explore the application of multi-scale temporal modeling to the small-footprint keyword spotting task. We propose a multi-branch temporal convolution module (MTConv), a CNN block consisting of multiple temporal convolution filters with different kernel sizes, which enriches temporal feature space. Besides, taking advantage of temporal and depthwise convolution, a temporal efficient neural network (TENet) is designed for KWS system. Based on the purposed model, we replace standard temporal convolution layers with MTConvs that can be trained for better performance. While at the inference stage, the MTConv can be equivalently converted to the base convolution architecture, so that no extra parameters and computational costs are added compared to the base model. The results on Google Speech Command Dataset show that one of our models trained with MTConv performs the accuracy of 96.8% with only 100K parameters.