在微型物联网设备上实现97%准确率的多关键词语音识别。
TinyML for Speech Recognition
- 用1D卷积神经网络+量化，在边缘设备部署语音识别模型。
- 在自建超1小时数据集上达到97%准确率，支持23个关键词。
- 基于Arduino Nano 33 BLE Sense，适合智能家居等低资源场景。
我们训练并部署了一个量化的1维卷积神经网络模型，以在高度资源受限的物联网边缘设备上实现语音识别。该技术可应用于智能家庭、老年人及残障人士的环境辅助生活等场景。本文首先构建了一个包含超过一小时音频数据的新数据集，为本研究及未来相关工作提供支持。其次，利用Edge Impulse技术提升模型性能，在自建数据集上实现了高达97%的准确率。验证阶段，我们基于Arduino Nano 33 BLE Sense微控制器板实现原型系统，该板专为物联网与人工智能应用设计，契合目标使用场景。与多数现有研究仅聚焦有限关键词不同，我们的模型可处理23个不同关键词，支持复杂指令输入。
原文摘要 · Abstract (English)
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.
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