通过语言相似性分析，提升低资源语言的跨语言语音表征效果
Improving Cross-Lingual Phonetic Representation of Low-Resource Languages Through Language Similarity Analysis
- 基于语音相似性筛选源语言，优化跨语言训练
- 使用语音相似语言使音素识别相对提升55.6%
- 适合低资源语言语音建模研究者参考
本文研究语言相似性对低资源语言跨语言语音表征的影响，强调源语言选择的重要性。以往跨语言研究常随意选取源语言，缺乏系统评估。本文提出一种实用方法，用于评估多语言家族间的语音接近度。研究发现，同语系内语言的语音相似性越高，多语言训练性能越好；而相似性较低时，性能反而低于单语训练。在音素识别任务中，采用语音相似语言可实现相对于单语训练55.6%的相对提升，甚至超过大规模自监督学习模型的表现。
原文摘要 · Abstract (English)
This paper examines how linguistic similarity affects cross-lingual phonetic representation in speech processing for low-resource languages, emphasizing effective source language selection. Previous cross-lingual research has used various source languages to enhance performance for the target low-resource language without thorough consideration of selection. Our study stands out by providing an in-depth analysis of language selection, supported by a practical approach to assess phonetic proximity among multiple language families. We investigate how within-family similarity impacts performance in multilingual training, which aids in understanding language dynamics. We also evaluate the effect of using phonologically similar languages, regardless of family. For the phoneme recognition task, utilizing phonologically similar languages consistently achieves a relative improvement of 55.6% over monolingual training, even surpassing the performance of a large-scale self-supervised learning model. Multilingual training within the same language family demonstrates that higher phonological similarity enhances performance, while lower similarity results in degraded performance compared to monolingual training.
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