Speaker Diarization with LSTM

Quan Wang, Carlton Downey, Li Wan, Philip Andrew Mansfield, Ignacio Lopez Moreno
10/28/2017
eess.AScs.LGcs.SD

Official code: wq2012/SpectralCluster (555 stars, Apache-2.0, last commit 2024-09-25).

Abstract

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.

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Code Implementations(1)

wq2012/SpectralClusterOfficial100%
Apache-2.0

Python re-implementation of the (constrained) spectral clustering algorithms used in Google's speaker diarization papers.

55575Shell, PythonJan 18, 20192 years agoApache-2.0
auto-tuneclusteringconstrained-clusteringmachine-learningpython+4 more

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Cite this paper

@article{wang2017speaker,
  title  = {Speaker Diarization with LSTM},
  author = {Quan Wang and Carlton Downey and Li Wan and Philip Andrew Mansfield and Ignacio Lopez Moreno},
  year   = {2017},
  eprint = {1710.10468},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1710.10468}
}

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