发表机构
Heinrich Heine University Düsseldorf; University of Florida（杜塞尔多夫海因里希·海涅大学; 佛罗里达大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究印地语-英语代码混合语音强制对齐，用蒙特利尔强制对齐器，解决自由变体和音素边界检测问题，自训练策略效果好，句子级代码混合数据训练的声学模型误差低，强调词典设计和训练数据对双语语音对齐的重要性。
AI 中文摘要
代码混合语音给强制对齐带来了独特挑战，如库存扩展、拼写错误和说话者差异。我们使用蒙特利尔强制对齐器评估印地语-英语代码混合语音的强制对齐。解决了两个问题：原生与非原生对的自由变体和话语中英语单词的音素边界检测。自训练策略明显优于未修改的词典。在句子级代码混合数据上训练的声学模型平均误差为4.15毫秒，比单语印地语或孤立英语替代方案低十倍。有原则的词典设计和代码混合训练数据对于双语语音的可靠对齐都至关重要。
英文摘要
Code-mixed speech poses unique challenges to forced alignment: expanded inventories, orthographic errors, and speaker variation. We evaluate forced alignment of Hindi-English code-mixed speech using the Montreal Forced Aligner. We address 2 problems: (1) free variation involving native vs non-native pairs and (2) phonemic boundary detection for mid-utterance English words. Bootstrapping strategies substantially outperform unmodified lexicons. Acoustic models trained on sentence-level code-mixed data achieve a mean error of 4.15ms, ie. ten times lower than monolingual Hindi (38.18ms) or isolated English (37.58ms) alternatives. Principled lexicon design and code-mixed training data are both essential for reliable alignment of bilingual speech.
