The advent of Internet of Things (IoT) technology has generated massive
interest in voice-controlled smart homes. While many voice-controlled smart
home systems are designed to understand and support widely spoken languages
like English, speakers of low-resource languages like Luganda may need more
support. This research project aimed to develop a Luganda speech intent
classification system for IoT applications to integrate local languages into
smart home environments. The project uses hardware components such as Raspberry
Pi, Wio Terminal, and ESP32 nodes as microcontrollers. The Raspberry Pi
processes Luganda voice commands, the Wio Terminal is a display device, and the
ESP32 nodes control the IoT devices. The ultimate objective of this work was to
enable voice control using Luganda, which was accomplished through a natural
language processing (NLP) model deployed on the Raspberry Pi. The NLP model
utilized Mel Frequency Cepstral Coefficients (MFCCs) as acoustic features and a
Convolutional Neural Network (Conv2D) architecture for speech intent
classification. A dataset of Luganda voice commands was curated for this
purpose and this has been made open-source. This work addresses the
localization challenges and linguistic diversity in IoT applications by
incorporating Luganda voice commands, enabling users to interact with smart
home devices without English proficiency, especially in regions where local
languages are predominant.

该研究项目旨在为物联网应用开发一种 Luganda 语音意图分类系统，从而将当地语言纳入智能家居环境中。