## 1 Introduction
In Reference [34] the Divergence Shape distance, described in Reference [35; 36], is computed for speaker change detection. After this, the audio is segmented using the speaker change detection. The conventional GMMs are trained to represent the speakers and a speaker identification is performed in order to decide which segment belongs to which speaker among the set of given target speakers. In this paper, we propose the use of RBM vectors [22] for the above mentioned tasks, that is, speaker clustering and speaker tracking. The RBM vector is extracted in several steps. First of all, a global or Universal RBM--referred to as URBM--is trained with all the available background data. Then, an adapted RBM model per test speaker is trained. In the case of speaker clustering, the test speakers are the segments that are to be clustered. In the case of speaker tracking, the test speakers are audio segments and target speakers. The visible to hidden weight matrices along with the visible and hidden bias vectors of these adapted RBMs are concatenated to generate RBM supervectors. The RBM supervectors are subjected to a Principal Component Analysis (PCA) whitening and dimensionality reduction to extract the desired RBM vectors. For the speaker clustering task, we extract RBM vectors using the method described above for the test speakers. In this way, all the speaker segments that are to be clustered are represented by RBM vectors. Then we cluster these RBM vectors by applying a bottom-up AHC approach using cosine and Probabilistic Linear Discriminant Analysis (PLDA) scores. In Reference [24], we have concluded that the RBM vector representation of speakers is successful in the task of speaker clustering. In this paper, we investigate the same RBM vector representation of speakers in the task of speaker tracking. For the speaker tracking task, we implement a two stage strategy. The first stage is based on speaker change detection by using Divergence Shape distance as in Reference [34]. The audio is segmented according to these speaker change points. In the second stage, the segments generated are identified against all the target speakers, in order to specify which segment belongs to which target speaker. The target speakers are first enrolled in the system. We represent all the segments and target speakers by RBM vectors. Then, the RBM vectors of all the segments are scored against the RBM vectors of all the target speakers using cosine and PLDA scoring. We have found that the RBM vector representation of speakers is successful in both these tasks as in speaker verification. The experimental results show that the RBM vector outperforms the conventional i-vectors based systems using both the cosine and PLDA scoring methods. The rest of the paper is organized as follows: Section 2 explains the detailed procedure of the proposed vector representation of speakers by using RBMs; Section 3 contains a brief description of the speaker clustering system; Section 4 contains a detailed description of the fundamental stages of our speaker tracking system; Section 5 describes the experimental setup, the database used and how the experiments were carried out; the results obtained are discussed in Section 6; and finally, in Section 7, some conclusions are drawn as the findings of this paper.