# TinyML for Speech Recognition

arXiv: https://arxiv.org/abs/2504.16213 · HTML: https://mlsanity.com/paper/2504.16213/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Builds on (most cited first, 9 of 9)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [1D Convolutional Neural Networks and Applications: A Survey](https://mlsanity.com/paper/1905.03554/) (2019)
- [Edge Impulse: An MLOps Platform for Tiny Machine Learning](https://mlsanity.com/paper/2212.03332/) (2022)
- [TinyML: Tools, Applications, Challenges, and Future Research Directions](https://mlsanity.com/paper/2303.13569/) (2023)
- [On-Device Domain Learning for Keyword Spotting on Low-Power Extreme Edge Embedded Systems](https://mlsanity.com/paper/2403.10549/) (2024)
- [TinySV: Speaker Verification in TinyML with On-device Learning](https://mlsanity.com/paper/2406.01655/) (2024)
- [Supporting AI Engineering on the IoT Edge through Model-Driven TinyML](https://mlsanity.com/paper/2107.02690/) (2021)
- [Implementation Of Tiny Machine Learning Models On Arduino 33 BLE For Gesture And Speech Recognition](https://mlsanity.com/paper/2207.12866/) (2022)
- [Towards Contactless Elevators with TinyML using CNN-based Person Detection and Keyword Spotting](https://mlsanity.com/paper/2405.13051/) (2024)

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

- [From Tiny Machine Learning to Tiny Deep Learning: A Survey](https://mlsanity.com/paper/2506.18927/) (2025)
- [TinyNav: End-to-End TinyML for Real-Time Autonomous Navigation on Microcontrollers](https://mlsanity.com/paper/2603.11071/) (2026)

## Abstract (arXiv)

We train and deploy a quantized 1D convolutional neural network model to conduct speech recognition on a highly resource-constrained IoT edge device. This can be useful in various Internet of Things (IoT) applications, such as smart homes and ambient assisted living for the elderly and people with disabilities, just to name a few examples. In this paper, we first create a new dataset with over one hour of audio data that enables our research and will be useful to future studies in this field. Second, we utilize the technologies provided by Edge Impulse to enhance our model's performance and achieve a high Accuracy of up to 97% on our dataset. For the validation, we implement our prototype using the Arduino Nano 33 BLE Sense microcontroller board. This microcontroller board is specifically designed for IoT and AI applications, making it an ideal choice for our target use case scenarios. While most existing research focuses on a limited set of keywords, our model can process 23 different keywords, enabling complex commands.