This study explores how sentence types affect the Lombard effect and intelligibility enhancement, focusing on comparisons between natural and grid sentences. Using the Lombard Chinese-TIMIT (LCT) corpus and the Enhanced MAndarin Lombard Grid (EMALG) corpus, we analyze changes in phonetic and acoustic features across different noise levels. Our results show that grid sentences produce more pronounced Lombard effects than natural sentences. Then, we develop and test a normal-to-Lombard conversion model, trained separately on LCT and EMALG corpora. Through subjective and objective evaluations, natural sentences are superior in maintaining speech quality in intelligibility enhancement. In contrast, grid sentences could provide superior intelligibility due to the more pronounced Lombard effect. This study provides a valuable perspective on enhancing speech communication in noisy environments.

翻译：本研究探讨句子类型如何影响Lombard效应与可懂度增强，重点对比自然句与网格句的差异。基于Lombard Chinese-TIMIT（LCT）语料库与增强型汉语Lombard网格（EMALG）语料库，我们分析了不同噪声水平下语音与声学特征的变化。结果表明，网格句比自然句产生更显著的Lombard效应。随后，我们开发并测试了一种正常语音至Lombard语音的转换模型，该模型分别使用LCT和EMALG语料库进行训练。通过主客观评估发现，在可懂度增强任务中，自然句在保持语音质量方面表现更优；而网格句则因更显著的Lombard效应可能提供更佳的可懂度。本研究为提升噪声环境下的语音通信效果提供了有价值的视角。