I-vector and variability compensation techniques for mobile phone recognition

Mobile phone recognition consists of trying to identify the mobile phone brand or model, which is very important in forensic analysis. In this paper, we exploit the audio recordings to realize this task. Audio data conveys many information, one of them is related to the source of records, i.e., is a specific audio was captured by a given mobile phone. In order to separate between this particular information and the rest of audio content, we introduce the I-vector technique. Additionally, Linear Discriminant Analysis (LDA) and Within-Class Covariance Normalization (WCCN) methods were used in different ways to compensate the speaker and channel variability. We evaluate the performance of our proposed system by correct rate recognition based on Gaussian Probabilistic Linear Discriminant Analysis (GPLDA) and Cosine Similarity Score (CSS) methods. To train the mobile phone models and do tests, two datasets were prepared: the public MOBIPHONE database and a novel local database. Experiments done with different configurations demonstrate the effectiveness of I-vectors models with the combination of LDA – WCCN compensation techniques and CSS as a similarity measurement method. A correct recognition rates of 97.23 %, 98.91 %, and 99.45 % were obtained for MOBIPHONE, the local Same Environment (SE) and Different Environment (DE) databases, respectively.