Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno
For many years, i-vector based audio embedding techniques were the
dominant approach for speaker verification and speaker diarization
applications. However, mirroring the rise of deep learning in various
domains, neural network based audio embeddings, also known as d-vectors,
have consistently demonstrated superior speaker verification performance.
In this paper, we build on the success of d-vector based speaker
verification systems to develop a new d-vector based approach to speaker
diarization. Specifically, we combine LSTM-based d-vector audio embeddings
with recent work in non-parametric clustering to obtain a state-of-the-art
speaker diarization system. Our system is evaluated on three standard
public datasets, suggesting that d-vector based diarization systems offer
significant advantages over traditional i-vector based systems. We
achieved a 12.0% diarization error rate on NIST SRE 2000 CALLHOME, while
our model is trained with out-of-domain data from voice search logs.