Skip to Main content Skip to Navigation
Conference papers

Iterative PLDA Adaptation for Speaker Diarization

Abstract : This paper investigates iterative PLDA adaptation for cross-show speaker diarization applied to small collections of French TV archives based on an i-vector framework. Using the target collection itself for unsupervised adaptation, PLDA parameters are iteratively tuned while score normalization is applied for convergence. Performances are compared, using combinations of target and external data for training and adaptation. The experiments on two distinct target corpora show that the proposed framework can gradually improve an existing system trained on external annotated data. Such results indicate that performing speaker diarization on small collections of unlabeled audio archives should only rely on the availability of a sufficient boot-strap system, which can be incrementally adapted to every target collection. The proposed framework also widens the range of acceptable speaker clustering thresholds for a given performance objective.
Complete list of metadatas

Cited literature [21 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01433172
Contributor : Sylvain Meignier <>
Submitted on : Thursday, March 30, 2017 - 10:48:06 PM
Last modification on : Tuesday, June 19, 2018 - 11:50:04 AM

File

0572b.pdf
Publisher files allowed on an open archive

Identifiers

Collections

Citation

Gaël Le Lan, Delphine Charlet, Anthony Larcher, Sylvain Meignier. Iterative PLDA Adaptation for Speaker Diarization. Interspeech 2016, Sep 2016, San Francisco, United States. pp.2175 - 2179, ⟨10.21437/Interspeech.2016-572⟩. ⟨hal-01433172⟩

Share

Metrics

Record views

387

Files downloads

513