用音标比对单词发音，快速识别语音相似词
Parallel Needleman-Wunsch on CUDA to measure word similarity based on phonetic transcriptions
- 用并行化纽曼-沃尔什算法比对音标序列
- GPU加速使大规模发音比对速度显著提升
- 适合语言学研究和语音相似性分析
我们提出一种基于音标的单词相似性计算方法，采用纽曼-沃尔什算法。在Rust中实现，并通过CPU与GPU并行化处理，以高效应对大规模数据。GPU版本利用CUDA与cudarc库，获得显著性能提升。通过构建全连接图（节点为单词，边权重为发音相似度），结合聚类算法识别语音相近的词组。实验表明该方法有效揭示语言发音结构，可轻松扩展至其他语言。
原文摘要 · Abstract (English)
We present a method to calculate the similarity between words based on their phonetic transcription (their pronunciation) using the Needleman-Wunsch algorithm. We implement this algorithm in Rust and parallelize it on both CPU and GPU to handle large datasets efficiently. The GPU implementation leverages CUDA and the cudarc Rust library to achieve significant performance improvements. We validate our approach by constructing a fully-connected graph where nodes represent words and edges have weights according to the similarity between the words. This graph is then analyzed using clustering algorithms to identify groups of phonetically similar words. Our results demonstrate the feasibility and effectiveness of the proposed method in analyzing the phonetic structure of languages. It might be easily expanded to other languages.
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