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This content will become publicly available on July 8, 2026

Title: TinyML for Speech Recognition
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.  more » « less
Award ID(s):
2349452
PAR ID:
10657370
Author(s) / Creator(s):
 ;  
Publisher / Repository:
IEEE
Date Published:
Page Range / eLocation ID:
1631 to 1636
Subject(s) / Keyword(s):
tinyml, speech recognition, arduino, machine learning, edge analytics
Format(s):
Medium: X
Location:
Toronto, Canada
Sponsoring Org:
National Science Foundation
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