Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized
Maximum Eigengap
- URL: http://arxiv.org/abs/2003.02405v1
- Date: Thu, 5 Mar 2020 02:50:37 GMT
- Title: Auto-Tuning Spectral Clustering for Speaker Diarization Using Normalized
Maximum Eigengap
- Authors: Tae Jin Park, Kyu J. Han, Manoj Kumar, Shrikanth Narayanan
- Abstract summary: We propose a new spectral clustering framework that can auto-tune the parameters of the clustering algorithm in the context of speaker diarization.
A relative improvement of 17% in the speaker error rate on the well-known CALLHOME evaluation set shows the effectiveness of our proposed spectral clustering with auto-tuning.
- Score: 43.82618103722998
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In this study, we propose a new spectral clustering framework that can
auto-tune the parameters of the clustering algorithm in the context of speaker
diarization. The proposed framework uses normalized maximum eigengap (NME)
values to estimate the number of clusters and the parameters for the threshold
of the elements of each row in an affinity matrix during spectral clustering,
without the use of parameter tuning on the development set. Even through this
hands-off approach, we achieve a comparable or better performance across
various evaluation sets than the results found using traditional clustering
methods that apply careful parameter tuning and development data. A relative
improvement of 17% in the speaker error rate on the well-known CALLHOME
evaluation set shows the effectiveness of our proposed spectral clustering with
auto-tuning.
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