摘要
为了高效地从大词汇量连续语音识别(LVCSR)的多候选中得到关键词结果,保证最小词错误率,提出了将混淆网络的思想应用到关键词检出系统中.在传统混淆网络生成方法基础上,提出一种改进的更加适合于关键词检出的关键词混淆网络作为关键词检出的中间结构,该方法只对所有关键词竞争候选生成带有得分标记的关键词混淆网络,突出候选之间竞争关系,并根据得分标记确定关键词.与传统的N-best作为中间结构的关键词检出系统比较,基于混淆网络的关键词检出系统的召回率为87.11%,提高了21.65%.实验表明,在提高召回率的同时,所提方法具有关键词直接定位的特点,因此具有较低的时间开销.
In order to achieve a higher keyword recall rate from large vocabulary continuous speech recognition （LVCSR） and minimize the word error rate, a confusion network was used in a keyword spotting system. Moreover, an improved method of generating a keyword confusion network which was more suitable for keyword spotting was proposed based on the traditional algorithm. This method only focused on keyword competitions, and was capa- ble of transforming all the key-word competitions into a confusion network with a marked score, and highlighted com- petitions to all the candidates. Compared with the traditional keyword spotting system which uses N-best as the me- dium structure, the proposed method increased the recall rate of confusion network to 87.11% ; compared with the keyword spotting system based on N-best, there is a 21.65% improvement in the recall rate. Experiments show the proposed method could locate keywords directly, besides increasing the recall rate, so the system costs less time.
出处
《智能系统学报》
2010年第5期432-435,共4页
CAAI Transactions on Intelligent Systems
基金
国家自然科学基金资助项目(60702053)
黑龙江省青年骨干教师支持计划资助项目(1155G17)
关键词
关键词检出
混淆网络
语音识别
keyword spotting
confusion network
speech recognition
