Article(id=1317501506585210944, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20260168, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792046437163, onlineDateStr=2026-10-15, pubDate=1791129600000, pubDateStr=2026-10-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792046437163, onlineIssueDateStr=2026-10-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792046437163, creator=13701087609, updateTime=1792046437163, updator=13701087609, issue=Issue{id=1317501484401541130, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='10', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1791129600000', pubDateStr='2026-10-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1792046431875, creator='13701087609', updateTime=1792121205555, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1317815108026356240, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1317815108026356241, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1317501484401541130, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=9, endPage=15, ext={EN=ArticleExt(id=1317501506983669825, articleId=1317501506585210944, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=An Intelligent Testing System for Cockpit Voice Interaction Driven by Multimodal Large Models, columnId=null, journalTitle=Automotive Digest, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the limitation that conventional voice testing methods fail to effectively evaluate the response capability of intelligent cockpit voice interaction systems, this paper presents an intelligent voice testing system driven by a Large Language Model (LLM). The system first employs the LLMs to perform semantic generalization on base voice commands and, in combination with a multimodal large model, synthesizes test audio that covers multiple timbres and speaking rates, thereby overcoming the constraints inherent in conventional fixed-form test case design. A parameterized mapping model between test cases and execution programs is then constructed, which decouples the test logic from the execution environment and substantially reduces the dependence of execution-program development on hardware resources. In the result verification stage, data from three independent dimensions (including audio feedback recognition, visual parsing of the head-unit display, and controller log analysis) are fused through a weighted decision-fusion model, thereby enhancing the objectivity and reliability of the verdicts. Tests are conducted on the cockpit voice-based music control function for validation, and the results demonstrate that the proposed system performs excellently in terms of test pass rate, decision accuracy, semantic consistency, and generalization diversity, confirming the effectiveness of this method in improving test coverage, efficiency, and confidence.

, authors=Jiaming Zhang, Yan Gao, Deyin Zhao, Mengmeng Xu, Cheng Qin, Sixu Li, Dongbo Zhang, authorsList=Jiaming Zhang, Yan Gao, Deyin Zhao, Mengmeng Xu, Cheng Qin, Sixu Li, Dongbo Zhang, authorCompany=null, correspAuthors=Yan Gao, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1317501507382128706, articleId=1317501506585210944, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=多模态大模型驱动的座舱语音智能化测试系统, columnId=1317501485198458892, journalTitle=汽车文摘, columnName=2026中国汽车工程学会年会优秀论文（选登）, runingTitle=null, highlight=null, articleAbstract=

针对传统语音测试方法难以有效评估智能座舱语音交互系统应答能力的问题，提出了一种大模型（LLM）驱动的语音智能化测试系统。该系统首先利用LLM对基础语音指令进行语义泛化，并结合多模态大模型合成覆盖多音色、多语速的测试音频，以突破传统测试用例设计局限。随后，构建了用例-执行程序参数化映射模型，实现了测试逻辑与执行环境的解耦，显著降低了执行程序开发对硬件资源的依赖。在测试结果验证阶段，融合音频反馈识别、车机屏幕视觉解析与控制器日志分析3个独立维度的数据，通过加权融合决策模型提升判别结果的客观性与可靠性。试验选取座舱语音音乐控制功能进行验证，结果表明本系统在测试通过率、判决准确率、语义一致性及泛化多样性等指标上表现优异，证实了该方法在提升测试覆盖度、效率与置信度方面的有效性。

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