Short- and Long-Term Speech Features for Hybrid HMM-i-Vector based Speaker Diarization System

i-vectors have been successfully applied over the last years in speaker recognition tasks. This work aims at assessing the suitability of i-vector modeling within the frame of speaker diarization task. In such context, a weighted cosine-distance between two different sets of i-vectors is proposed for speaker clustering. Speech clusters generated by Viterbi segmentation are first modeled by two different i-vectors. Whilst the first i-vector represents the distribution of the commonly used short-term Mel Frequency Cepstral Coefficients, the second one depicts a selection of voice quality and prosodic features. In order to combine both the shortand long-term speech features, the cosinedistance scores of those two i-vectors are linearly weighted to obtain a unique similarity score. The final fused score is then used as speaker clustering distance. Our experimental results on two different evaluation sets of the Augmented Multi-party Interaction corpus show the suitability of combining both sources of information within the i-vector space. Our experimental results show that the use of i-vector based clustering technique provides a significant improvement, in terms of diarization error rate, than those based on Gaussian Mixture Modeling technique. Furthermore, this work also reports a significant speaker error reduction by augmenting i-vectors extracted from shortterm spectral features with a second i-vector extracted from voice quality and prosody related speech features.