摘要
随着人工智能的发展,语音交互技术在智能家居、智能助理等领域得到长足的发展,口语化短文本的语义理解技术成为研究重点。口语文本中存在句法不规范、关键信息少、句子间差异小等特点,影响语义识别准确率,模型需不断更新迭代。本文将层次化理论与智能家居领域语音交互应用相结合,形成多层语义解析架构,实现文本数据到结构化知识的转变,在少量文本数据的基础上,实现训练耗时10秒以内,平均准确率96%以上,并设计了分布式模型训练系统,满足工业级应用需求。
With the development of artificial intelligence,speech interaction technology has made great progress in smart home,intelligent assistant and other fields.The semantic understanding technology of oral short text has become the research focus.There are some characteristics in spoken text,such as nonstandard syntax,less key information and small differences between sentences,which affect the accuracy of semantic recognition.In this paper,the hierarchical theory is combined with the voice interaction application in smart home field to form a multi-layer semantic analysis framework,which realizes the transformation from text data to structured knowledge.On the basis of a small amount of text data,the training time is less than 10 seconds,and the average accuracy rate is more than 96%.A distributed model training system is designed to meet the industrial application requirements.
作者
李明杰
贾巨涛
宋德超
吴伟
韩林峄
LI Mingjie;JIA Jutao;SONG Dechao;WU Wei;HAN Linyi(GREE ELECTRIC APPLIANCES,INC.OF ZHUHAI,Zhuhai 519070;State Key Laboratory of Air-conditioning Equipment and System Energy Conservation,Zhuhai 519070)
出处
《家电科技》
2020年第S01期222-224,共3页
Journal of Appliance Science & Technology
关键词
语音交互
口语语义
少量数据
层次化
Voice interaction
Oral semantics
A small amount of data
Hierarchical
