Mobile phone clustering from acquired speech recordings using deep Gaussian supervector and spectral clustering
Yanxiong Li, Xue Zhang, Xianku Li, Xiaohui Feng, Jichen Yang, Aiwu Chen, Qianhua He · 2017
Acquisition device clustering from speech recordings is a new and critical problem in the field of speech forensic, which aims at merging speech recordings acquired by the same device into one cluster without both pre-knowing prior information of the processed data and pre-training classifier. We propose a mobile phone clustering method, in which deep Gaussian supervector learned by deep neural network is used to represent the intrinsic trace left behind by mobile phone in speech recordings, and then spectral clustering technique is adopted to merge speech recordings acquired by the same mobile phone into one cluster. The performance of the proposed method is evaluated on a public corpus of speech recordings acquired by mobile phones. The results show that the proposed method is effective for mobile phone clustering from acquired speech recordings.