Title: Fotheidil: an Automatic Transcription System for the Irish Language Show Chinese title

Title: Fotheidil：爱尔兰语的自动转录系统

Abstract: This paper sets out the first web-based transcription system for the Irish language - Fotheidil, a system that utilises speech-related AI technologies as part of the ABAIR initiative. The system includes both off-the-shelf pre-trained voice activity detection and speaker diarisation models and models trained specifically for Irish automatic speech recognition and capitalisation and punctuation restoration. Semi-supervised learning is explored to improve the acoustic model of a modular TDNN-HMM ASR system, yielding substantial improvements for out-of-domain test sets and dialects that are underrepresented in the supervised training set. A novel approach to capitalisation and punctuation restoration involving sequence-to-sequence models is compared with the conventional approach using a classification model. Experimental results show here also substantial improvements in performance. The system will be made freely available for public use, and represents an important resource to researchers and others who transcribe Irish language materials. Human-corrected transcriptions will be collected and included in the training dataset as the system is used, which should lead to incremental improvements to the ASR model in a cyclical, community-driven fashion. Show Chinese abstract

Abstract: 本文介绍了第一个基于网络的爱尔兰语转录系统 - Fotheidil，这是一个利用与语音相关的AI技术的系统，作为ABAIR倡议的一部分。 该系统包括现成的预训练语音活动检测和说话人分割模型，以及专门为爱尔兰语自动语音识别和大写及标点恢复训练的模型。 探索了半监督学习以改进模块化TDNN-HMM ASR系统的声学模型，对于监督训练集中代表性不足的域测试集和方言，取得了显著的改进。 一种涉及序列到序列模型的大小写和标点恢复新方法与使用分类模型的传统方法进行了比较。 实验结果也显示了性能的显著提升。 该系统将免费提供给公众使用，并且是研究人员和其他转录爱尔兰语材料的人的重要资源。 在系统使用过程中，将收集人工校正的转录文本并包含在训练数据集中，这应该能以循环的、社区驱动的方式逐步改进ASR模型。