首个爱尔兰语在线语音转录系统,支持自动识别与标点修复。
Fotheidil: an Automatic Transcription System for the Irish Language
- 结合预训练模型与定制AI,实现爱尔兰语语音识别与标点恢复。
- 半监督学习使跨方言、低资源场景下识别准确率显著提升。
- 支持社区协作迭代优化,适合语言研究与文化保护者使用。
本文介绍首个基于网页的爱尔兰语语音转录系统Fotheidil,该系统是ABAIR计划的一部分,采用语音相关AI技术。系统包含现成的语音活动检测与说话人分离模型,以及针对爱尔兰语训练的自动语音识别(ASR)、大写与标点恢复模型。通过半监督学习改进模块化TDNN-HMM ASR系统的声学模型,在域外测试集和训练数据中代表性不足的方言上均取得显著提升。一种基于序列到序列模型的大写与标点恢复方法,相比传统分类模型也表现出明显优势。系统将免费向公众开放,同时收集人工校正的转录数据以持续优化训练集,形成社区驱动的循环改进机制。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。