摘要
已有语音识别方法将用户用英文语音表达的任务目标直接施加到模糊自适应环中,采取直接将识别结果匹配规则前件的方法,限制了系统的识别能力。为此,提出一种语音式任务目标的结构化转换方法。对于语音式任务目标进行句法分析和关键成分提取,对关键成分进行语义关联拓展,建立与任务目标等价的语义关联集合,基于集合完成面向模糊规则前件的结构化转换。通过搭建任务机器人实验系统,验证了该方法具有较好的语音式任务目标识别能力。
The existing voice-identification methods directly apply English voice-based task goals into the fuzzy self-adaptation loop.However,the ability of recognitionis limited,as the methods directly matched the raw recognized phrases with rules.In order to solve this problem,a structural transformation approach is proposed.Firstly,voice-based task goals are analyzed through the syntax and their key components are extracted.Then,semantic-equivalence sets are established by expanding semantic-relevance words.Based on these keyword sets of task goals,structural transformation orienting to fuzzy rules'pre-component is finally completed.By constructing task-oriented robot system,it is verified the approach has better recognition ability of voice-based task goals.
作者
张晓冰
杨启亮
邢建春
韩德帅
ZHANG Xiaobing;YANG Qiliang;XING Jianchun;HAN Deshuai(College of Defense Engineering,PLA University of Science and Technology,Nanjing 210007,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2018年第4期59-65,共7页
Computer Engineering
基金
江苏省自然科学基金面上项目(BK20151451)
关键词
自适应软件系统
软件模糊自适应
目标识别
结构化转换
自然语言处理
self-adaptive software system
Software Fuzzy Self-adaptation(SFSA)
object recognition
structured conversion
Natural Language Processing(NLP)
