基于隐马尔科夫模型集成学习的广播关键词检测

Application of Hidden Markov Model with Ensemble Learning in Broadcast Keyword Detection

由于无线电技术的日益成熟,盗用正常广播频段进行其他活动的非法广播对国民经济和安全造成相当大的危害,因此对非法广播的监测非常重要。本文集成隐马尔科夫模型对广播关键词进行识别,进而监测非法广播。在试验中,首先对采集的非法广播进行人工切割与标定用于训练,然后研究基于集成学习的方法组合多个模型,使用投票规则得到最终结果。将集成学习的PocketSphinx系统与单一模型进行比较,试验结果显示,与单一模型84.8%的识别率相比,集成的PocketSphinx系统识别率达到92%,并且具有更好的稳定性。

Due to the increasing maturity of radio technology,illegal broadcasts that use the normal broadcast band for other activities have caused considerable harm to the national economy and security,so the monitoring of illegal broadcasts is very important.This paper integrated hidden Markov models to identify broadcast keywords,and then monitored illegal broadcasts.In the experiment,the illegal broadcasts collected were first manually cut and calibrated for training,then the method based on ensemble learning was used to combine multiple models,and the voting rules were used to obtain the final results.Comparing the integrated learning PocketSphinx system with a single model,the experimental results show that compared with the single model’s 84.8%recognition rate,the integrated PocketSphinx system has a recognition rate of 92%and has better stability.