Phoneme Background Model for Information Bottleneck based Speaker Diarization

Abstract Acoustic variability of speakers arises due to differences in their vocal tract characteristics. These individual speaker characteristics are reflected in a speech signal when speakers pronounce a given phoneme. The current work hypothesizes that clusters within a phoneme spoken by multiple speakers roughly correspond to different speakers. Based on this hypothesis, a Gaussian mixture model (GMM) based phoneme background model (PBM) is estimated. The components of such a PBM are used as a set of relevance variables in information bottleneck based speaker diarization system. Experiments are done using phone transcripts obtained from ground-truth and automatic speech recognition (ASR) system to estimate the PBM. The diarization experiments done on meeting recordings from AMI and NIST-RT corpora show that the proposed method achieves significant improvements over the system using a background model which ignores phoneme information