Combining SGMM speaker vectors and KL-HMM approach for speaker diarization

In this paper, a method to use SGMM speaker vectors for speaker diarization is introduced. The architecture of the Information Bottleneck (IB) based speaker diarization is utilized for this purpose. The audio for speaker diarization is split into short uniform segments. Speaker vectors are obtained from a Subspace Gaussian Mixture Model (SGMM) system trained on meeting data. The speaker vectors are clustered using the K-means algorithm. Two types of distance measures are explored in the clustering step: cosine distance of the speaker vectors and that of the vectors in a space projected by Probabilistic Linear Discriminant Analysis (PLDA). The clustering output is used as an initialization step for the Kullback Leibler-Hidden Markov Model (KL-HMM) based speech segmentation approach commonly used in the IB system for diarization. The proposed method is compared to clustering the segments using the IB based approach. A relative improvement of approximately 14% is obtained on the diarization performance for the proposed approach using SGMM speaker vectors with PLDA on the NIST RT 09 dataset.