Code understanding is a foundational capability in software engineering tools and developer workflows. However, most existing systems are designed for English-speaking users interacting via keyboards, which limits accessibility in multilingual and voice-first settings, particularly in regions like India. Voice-based interfaces offer a more inclusive modality, but spoken queries involving code present unique challenges due to the presence of non-standard English usage, domain-specific vocabulary, and custom identifiers such as variable and function names, often combined with code-mixed expressions. In this work, we develop a multilingual speech-driven framework for code understanding that accepts spoken queries in a user native language, transcribes them using Automatic Speech Recognition (ASR), applies code-aware ASR output refinement using Large Language Models (LLMs), and interfaces with code models to perform tasks such as code question answering and code retrieval through benchmarks such as CodeSearchNet, CoRNStack, and CodeQA. Focusing on four widely spoken Indic languages and English, we systematically characterize how transcription errors impact downstream task performance. We also identified key failure modes in ASR for code and demonstrated that LLM-guided refinement significantly improves performance across both transcription and code understanding stages. Our findings underscore the need for code-sensitive adaptations in speech interfaces and offer a practical solution for building robust, multilingual voice-driven programming tools.

翻译：代码理解是软件工程工具与开发者工作流中的基础能力。然而，现有系统大多为使用键盘交互的英语用户设计，这限制了其在多语言及语音优先场景下的可访问性，尤其在印度等地区。基于语音的界面提供了更具包容性的交互方式，但涉及代码的口语查询因非标准英语用法、领域特定词汇以及变量名和函数名等自定义标识符（常与代码混合表达结合）的存在而面临独特挑战。本研究开发了一个用于代码理解的多语言语音驱动框架，该框架接受用户母语的口语查询，使用自动语音识别（ASR）进行转录，通过大型语言模型（LLMs）进行代码感知的ASR输出优化，并与代码模型对接以执行代码问答和代码检索等任务（基于CodeSearchNet、CoRNStack和CodeQA等基准测试）。聚焦于四种广泛使用的印度语言及英语，我们系统性地分析了转录错误如何影响下游任务性能。同时，我们识别了ASR处理代码时的关键失效模式，并证明LLM引导的优化能显著提升转录和代码理解阶段的性能。我们的研究结果强调了语音界面进行代码敏感适配的必要性，并为构建鲁棒的多语言语音驱动编程工具提供了实用解决方案。