Improving i-Vector and PLDA Based Speaker Clustering with Long-Term Features

i-vector modeling techniques have been successfully used for speaker clustering task recently. In this work, we propose the extraction of i-vectors from shortand long-term speech features, and the fusion of their PLDA scores within the frame of speaker diarization. Two sets of i-vectors are first extracted from short-term spectral and long-term voice-quality, prosodic and glottal to noise excitation ratio (GNE) features. Then, the PLDA scores of these two i-vectors are fused for speaker clustering task. Experiments have been carried out on single and multiple site scenario test sets of Augmented Multi-party Interaction (AMI) corpus. Experimental results show that i-vector based PLDA speaker clustering technique provides a significant diarization error rate (DER) improvement than GMM based BIC clustering technique.