基于音素增强思维链的语音转文本翻译：提升低资源场景下的跨语言迁移能力
Speech-to-Text Translation with Phoneme-Augmented CoT: Enhancing Cross-Lingual Transfer in Low-Resource Scenarios
AI总结：
本研究提出将音素表示融入思维链框架的语音转文本翻译方法，通过课程学习训练扩展后的多语种大语言模型，提升低资源场景翻译质量并实现零资源翻译，为多语言S2TT普及提供新思路。
摘要：
我们提出一种语音转文本翻译（Speech-to-Text Translation, S2TT）方法，将音素表示融入思维链（Chain-of-Thought, CoT）框架，以改善低资源和零资源场景下的翻译效果。通过引入音素识别作为中间步骤，我们增强了跨语言迁移能力，甚至能实现无标注语音数据语言的翻译。该系统以多语种大语言模型（LLM）为基础，我们对其进行扩展以处理语音和音素。训练采用课程学习策略，逐步引入更复杂的任务。在多语种S2TT基准上的实验表明，音素增强CoT在低资源条件下提升了翻译质量，并支持零资源翻译，但对高资源性能有轻微影响。尽管存在这种权衡，我们的研究结果表明，基于音素的CoT是推动S2TT在更多语言中普及的一个有前景的方向。
英文摘要：
We propose a Speech-to-Text Translation (S2TT) approach that integrates phoneme representations into a Chain-of-Thought (CoT) framework to improve translation in low-resource and zero-resource settings. By introducing phoneme recognition as an intermediate step, we enhance cross-lingual transfer, enabling translation even for languages with no labeled speech data. Our system builds on a multilingual LLM, which we extend to process speech and phonemes. Training follows a curriculum learning strategy that progressively introduces more complex tasks. Experiments on multilingual S2TT benchmarks show that phoneme-augmented CoT improves translation quality in low-resource conditions and enables zero-resource translation, while slightly impacting high-resource performance. Despite this trade-off, our findings demonstrate that phoneme-based CoT is a promising step toward making S2TT more accessible across diverse languages.
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