In-domain Adaptation Solutions for the RTVE 2018 Diarization Challenge

This paper tries to deal with domain mismatch scenarios in the diarization task. This research has been carried out in the con-text of the Radio Televisi´on Espa˜nola (RTVE) 2018 Challenge at IberSpeech 2018. This evaluation seeks the improvement of the diarization task in broadcast corpora, known to contain multiple unknown speakers. These speakers are set to contribute in different scenarios, genres, media and languages. The evaluation offers two different conditions: A closed one with restrictions in the resources to train and develop diarization systems, and an open condition without restrictions to check the latest improvements in the state-of-the-art. Our proposal is centered on the closed condition, specially dealing with two important mismatches: media and language. ViVoLab system for the challenge is based on the i-vector PLDA framework: I-vectors are extracted from the input audio according to a given segmentation, supposing that each segment represents one speaker intervention. The diarization hypotheses are obtained by clustering the estimated i-vectors with a Fully Bayesian PLDA, a generative model with latent variables as speaker labels. The number of speakers is decided by com-paring multiple hypotheses according to the Evidence Lower Bound (ELBO) provided by the PLDA, penalized in terms of the hypothesized speakers to compensate different modeling ca-pabilities.