面向软件模糊自适应的语音式任务目标识别与结构化转换

Recognization and Structured Conversion of Phonetic Form Task Object for Software Fuzzy Self-adaptation

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.