Integrating online i-vector extractor with information bottleneck based speaker diarization system
Srikanth R. Madikeri, Ivan Himawan, Petr Motlíček, Marc Ferràs · 2015
Conventional approaches to speaker diarization use short-term features such as Mel Frequency Cepstral Co-efficients (MFCC).Features such as i-vectors have been used on longer segments (minimum 2.5 seconds of speech).Using i-vectors for speaker diarization has been shown to be beneficial as it models speaker information explicitly.In this paper, the i-vector modelling technique is adapted to be used as short term features for diarization by estimating i-vectors over a short window of MFCCs.The Information Bottleneck (IB) approach provides a convenient platform to integrate multiple features together for fast and accurate diarization of speech.Speaker models are estimated over a window of 10 frames of speech and used as features in the IB system.Experiments on the NIST RT datasets show absolute improvements of 3.9% in the best case when ivectors are used as auxiliary features to MFCC.Further, discriminative training algorithms such as LDA and PLDA are applied on the i-vectors.A best case performance improvement of 5% in absolute terms is obtained on the RT datasets.