一种面向移动终端的自然口语任务理解方法

随着移动互联时代的到来和语音识别技术的日益成熟,通过语音的交互方式来使用移动终端成为一种趋势.如何理解用户自然状态下的口语输入,传统的做法是手写上下文无关的文法规则,但是文法规则的书写需耗费大量的人力和物力,很难去维护和更新.提出一种采用支持向量机和条件随机场串行结合的方法,把口语任务理解分解为任务发现和信息抽取两个过程,并最终将任务表达成语义向量的形式.最终对“讯飞语点”语音助手用户返回的八个不同的任务种类的数据进行了测试,在一比一的噪声中识别任务语义表达的准确率为90.29%,召回率为88.87%.

A Method to Understand Spontaneous Spoken Tasks for Mobile Terminals

With the development of mobile Internet and automatic speech recognition (ASR), the mobile terminal through voice interaction has become a trend. The traditional method to understand user's spontaneous spoken language is to write context-free grammars(CFGs)manually. But it is laborious and expensive to construct a grammar with good coverage and optimized performance, and difficult to maintain and update. We proposed a new approach to spoken language understanding combining support vector machine(SVM)and conditional random fields(CRFs), which detect task and extract task semantic information from spontaneous speech input respectively. Tasks are represented as a vector of task name and semantic information. Eight different tasks from "iFLYTEK yudian" voice mobile assistant are tested, and the precision and recall of semantic representation of query are 90.29% and 88.87% respectively.