基于邻域保持嵌入算法的语种识别

Language recognition based on neighborhood preserving embedding

语种识别中现有的总变化因子分析仅能反映语音数据的整体结构,不能挖掘其局部内在结构信息,并且未考虑训练语音数据的语种类别。针对此问题,提出了基于邻域保持嵌入算法的语种识别,通过构建邻接图以获得语音数据的局部邻域结构,同时通过有监督训练有效利用语音数据的语种标注信息。在2011年美国国家标准与技术研究院语种识别评测的30 s和10 s测试集上进行了对比实验。实验结果表明,邻域保持嵌入算法能够有效弥补总变化因子分析的不足,可明显提高系统的识别性能。

The state-of-the-art total variability factor analysis in language recognition can only preserve the global structure of speech data, without mining the local structure information, and the language of training speech data is not considered. To solve the problem, neighborhood preserving embedding(NPE) algorithm is introduced into the language recognition system. NPE can preserve the local neighborhood structure of speech data by constructing a neighborhood graph. As well,NPE can effectively use the language label information of speech data by supervised training. The proposed method is compared with the total variability factor analysis in 30 s and 10 s tasks of the NIST 2011 language recognition evaluation(LRE) dataset. The experimental results indicate that the proposed NPE method can overcome the deficiency of total variability factor analysis and improve the system performance significantly.