Investigating Deep Neural Networks for Speaker Diarization in the DIHARD Challenge

Abstract

We investigate the use of deep neural networks (DNNs) for the speaker diarization task to improve performance under domain mismatched conditions. Three unsupervised domain adaptation techniques, namely inter-dataset variability compensation (IDVC), domain-invariant covariance normalization (DICN), and domain mismatch modeling (DMM), are applied on DNN based speaker embeddings to compensate for the mismatch in the embedding subspace. We present results conducted on the DIHARD data, which was released for the 2018 diarization challenge. Collected from a diverse set of domains, this data provides very challenging domain mismatched conditions for the diarization task. Our results provide insights into how the performance of our proposed system could be further improved.