Montreal Forced Aligner 与 2026 年语音到文本对齐的现状
Montreal Forced Aligner and the state of speech-to-text alignment in 2026
AI总结：
本文介绍 MFA 3.0 自 1.0 版本以来的发展，并在英语、日语和韩语上评估其性能，在四个基准数据集上达到平均边界误差低于 15 ms 的最优或接近最优性能。
摘要：
Montreal Forced Aligner (MFA) 于 2016 年发布，此后成为研究和工业中最广泛使用的强制对齐工具。在过去的十年中，MFA 经历了实质性发展，包括使用更大的开源数据集扩展到更多语言和方言、统一的 IPA 词典、模型自适应、跨语言音素映射以及支持工具。本文记录了 MFA 3.0 自 1.0 版本以来的发展，并在英语、日语和韩语上评估 MFA 的性能，与经典和神经强制对齐器进行基准测试。MFA 3.0 在所有四个基准数据集上实现了最优或接近最优的性能，平均边界误差低于 15 ms。自适应和跨语言映射对于 MFA 训练分布之外的语言有效，并且发音概率建模和音系规则在特定条件下提供了增益。
英文摘要：
The Montreal Forced Aligner (MFA) was released in 2016 and has since become the most widely used tool for forced alignment in research and industry. In the decade since, MFA has undergone substantial development, including expanded coverage across more languages and dialects using larger open-source datasets, harmonized IPA dictionaries, model adaptation, cross-language phone remapping, and support utilities. This paper documents MFA 3.0's developments since version 1.0 and evaluates MFA's performance across English, Japanese, and Korean, benchmarked against classic and neural forced aligners. MFA 3.0 achieves state-of-the-art or near state-of-the-art performance across all four benchmark datasets with mean boundary errors below 15 ms. Adaptation and cross-language remapping are effective for languages outside MFA's training distribution, and pronunciation probability modeling and phonological rules provide gains in specific conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
