探究句子类型对伦巴第效应与可懂度增强的影响：自然句子与网格句子的对比研究
Exploring Sentence Type Effects on the Lombard Effect and Intelligibility Enhancement: A Comparative Study of Natural and Grid Sentences
AI总结：
该研究对比自然与网格句子对伦巴第效应及语音可懂度增强的影响，发现网格句子伦巴第效应更显著、可懂度更高，自然句子语音质量维持更优，为噪声环境语音通信增强提供新视角。
摘要：
本研究探究句子类型如何影响伦巴第效应（Lombard effect）与可懂度增强，重点对比自然句子与网格句子的差异。研究使用Lombard Chinese-TIMIT（LCT，伦巴第中文TIMIT）语料库与Enhanced MAndarin Lombard Grid（EMALG，增强型普通话伦巴第网格）语料库，分析不同噪声水平下语音与声学特征的变化。结果显示，网格句子产生的伦巴第效应比自然句子更显著。随后，我们开发并测试了正常语音转伦巴第语音的转换模型，分别在LCT与EMALG语料库上训练。通过主客观评估，自然句子在可懂度增强中维持语音质量的表现更优；相比之下，网格句子因伦巴第效应更显著，可提供更高的可懂度。本研究为提升噪声环境下的语音通信提供了有价值的视角。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
