摘要
提出了使用具有模拟随机时序数据良好能力的隐马尔可夫链来完成广播新闻分割分类的算法 .首先使用含隐藏语义状态的隐马尔可夫链把原始广播新闻粗略分割分类成开始 /结束和语音两部分 ,其次应用 3个隐马尔可夫链 ,按照最大似然概率法把语音片段预识别为主持人介绍、广告和天气预报 ,最后由语义变化速率识别出新闻现场报道 ,完成广播新闻的精细分割分类任务 .
A new HMM-based segmentation and classification algorithm is proposed for the segmentation and classification of broadcast news since HMM can simulate stochastic time series data quite well. Firstly, by using an HMM, which has two hidden semantic states, the raw broadcast news is coarse-grained segmented into two parts: prelude/finale and speech. Then three HMMs are used to pre-classify speech clips as anchorpersons, commercials and weather forecasts based on maximum probability. Finally the change of semantic rate is checked to identify the detailed report.
出处
《计算机研究与发展》
EI
CSCD
北大核心
2002年第9期1057-1063,共7页
Journal of Computer Research and Development
基金
教育部博士点科研基金 ( 2 0 0 10 335 0 49)
教育部优秀年轻教师基金
高等学校骨干教师资助计划资助
