论文标题
来自非线性功能的孟加拉语的演讲者认可
Speaker Recognition in Bengali Language from Nonlinear Features
论文作者
论文摘要
目前，由于其不同的应用程序，自动说话者识别系统是一个非常重要的问题。因此，绝对有必要获得考虑一个人的口语风格，人声信息，声音的音质品质以及有关他声音的其他先天性信息的模型。孟加拉语音识别和说话者认同的研究在文献中很少。因此，需要使孟加拉受试者参与对我们的说话者识别引擎进行建模。在这项工作中，我们使用非线性多重分析提取了语音的一些声学特征。多重划分的下降波动分析基本上揭示了与所采用的语音信号相关的复杂性。源特性已经借助不同技术（例如相关矩阵，MFDFA频谱等偏度等）进行了量化。从这项研究中获得的结果为孟加拉语说话者提供了良好的识别率。
At present Automatic Speaker Recognition system is a very important issue due to its diverse applications. Hence, it becomes absolutely necessary to obtain models that take into consideration the speaking style of a person, vocal tract information, timbral qualities of his voice and other congenital information regarding his voice. The study of Bengali speech recognition and speaker identification is scarce in the literature. Hence the need arises for involving Bengali subjects in modelling our speaker identification engine. In this work, we have extracted some acoustic features of speech using non linear multifractal analysis. The Multifractal Detrended Fluctuation Analysis reveals essentially the complexity associated with the speech signals taken. The source characteristics have been quantified with the help of different techniques like Correlation Matrix, skewness of MFDFA spectrum etc. The Results obtained from this study gives a good recognition rate for Bengali Speakers.
