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- W2896538040 abstract "In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different speakers interleave in the time domain. This RNN is naturally integrated with a distance-dependent Chinese restaurant process (ddCRP) to accommodate an unknown number of speakers. Our system is fully supervised and is able to learn from examples where time-stamped speaker labels are annotated. We achieved a 7.6% diarization error rate on NIST SRE 2000 CALLHOME, which is better than the state-of-the-art method using spectral clustering. Moreover, our method decodes in an online fashion while most state-of-the-art systems rely on offline clustering." @default.
- W2896538040 created "2018-10-26" @default.
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- W2896538040 date "2019-05-01" @default.
- W2896538040 modified "2023-10-12" @default.
- W2896538040 title "Fully Supervised Speaker Diarization" @default.
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- W2896538040 doi "https://doi.org/10.1109/icassp.2019.8683892" @default.
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