基于语音识别的朝鲜语语音检索方法

针对基于语音识别的语音检索方法对语言模型的强依赖问题,通过改进声学模型学习框架提出了一种新的朝鲜语语音检索方法.该方法首先修改KoSpeech框架的网络模型,通过训练得到了朝鲜语的声学模型; 其次通过语音文档分割方法构建了语音文档索引库; 最后利用编辑距离匹配的方法实现了语音检索.实验结果表明,改进的朝鲜语声学模型学习框架降低了语音检索方法对语言模型的依赖和大规模数据集的要求.当k取9时, top -k评价方法的检索均值平均精度达到86.74%, 召回率达到95.25%, 该结果表明本文提出的方法是有效的,具有一定的实际应用价值.

Korean speech retrieval method based on speech recognition

Aiming the issue that recognition based speech retrieval method relies heavily on language model, a novel Korean speech retrieval method based on improved acoustic model learning framework is proposed. First, Korean acoustic model is trained by modified KoSpeech framework network model. Second, speech documents index library is constructed by speech document segementation method. Finally, Levenshtein distance matching method is used to implementation speech retrieval. Experiments result show that proposed improved model of Korean acoustic reduces the dependency of language model and the requirement of largescale dataset for retrieval method. For the top -k evaluation method, mAP and recall rate reach best to 86.74% and 95.25% respectively when k=9, so it is firmly demonstrated that the proposed method is effective and has certain practical application value.