Luganda Speech Intent Recognition for IoT Applications

TL;DR

This paper develops a Luganda speech intent recognition system for IoT devices, enabling voice-controlled smart homes in low-resource language settings using a CNN-based NLP model on Raspberry Pi.

Contribution

It introduces a novel Luganda speech intent classification system for IoT, utilizing a curated open-source dataset and deploying a CNN model on embedded hardware.

Findings

Successful implementation of Luganda voice command recognition on Raspberry Pi

Open-source Luganda voice command dataset created for NLP tasks

Enhanced local language support in IoT applications

Abstract

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.