SiangLao - Lao Voice-to-Text

Convert Lao speech to text with advanced AI models

Research Overview

  • Models Compared: XLS-R-300M, XLSR-53, and HuBERT-Large
  • Dataset: SiangLao/lao-asr-thesis-dataset
  • Best Performance: XLS-R-300M achieved 12.5% Character Error Rate (CER)
  • Training Method: 15 epochs with early stopping to prevent overfitting

Research Team

  • Authors: Souphaxay Naovalath & Sounmy Chanthavong
  • Advisor: Dr. Somsack Inthasone
  • Institution: Computer Science Department, Faculty of Natural Sciences, National University of Laos
ASR Model
  • Use clear and slow speech
  • Record in a quiet environment
  • Try different models to get the best results
  • Audio quality affects transcription accuracy

SiangLao ASR System

Computer Science Department • National University of Laos