OcrV1, Main, Exploration, bibRecord, 000225

Unsupervised Speaker Identification using Overlaid Texts in TV Broadcast

Identifieur interne : 000225 ( Main/Exploration ); précédent : 000224; suivant : 000226

Unsupervised Speaker Identification using Overlaid Texts in TV Broadcast

Auteurs : Johann Poignant [France] ; Hervé Bredin [France] ; Viet-Bac Le [France] ; Laurent Besacier [France] ; Claude Barras [France] ; Georges Quénot [France]

Source :

RBID : Hal:hal-00767427

English descriptors

mix :
- multimodal fusion, optical character recognition, reproducible results, speaker diarization, unsupervised speaker identification.

Abstract

We propose an approach for unsupervised speaker identification in TV broadcast videos, by combining acoustic speaker diarization with person names obtained via video OCR from overlaid texts. Three methods for the propagation of the overlaid names to the speech turns are compared, taking into account the co-occurence duration between the speaker clusters and the names provided by the video OCR and using a task-adapted variant of the TF-IDF information retrieval coefficient. These methods were tested on the REPERE dry-run evaluation corpus, containing 3 hours of annotated videos. Our best unsupervised system reaches a F-measure of 70.2% when considering all the speakers, and 81.7% if anchor speakers are left out. By comparison, a mono-modal, supervised speaker identification system with 535 speaker models trained on matching development data and additional TV and radio data only provided a 57.5% F-measure when considering all the speakers and 45.7% without anchor.

Url:

https://hal.archives-ouvertes.fr/hal-00767427

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 000127
to stream Hal, to step Curation: 000127
to stream Hal, to step Checkpoint: 000063
to stream Main, to step Merge: 000229
to stream Main, to step Curation: 000225

Le document en format XML

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<front><div type="abstract" xml:lang="en">We propose an approach for unsupervised speaker identification in TV broadcast videos, by combining acoustic speaker diarization with person names obtained via video OCR from overlaid texts. Three methods for the propagation of the overlaid names to the speech turns are compared, taking into account the co-occurence duration between the speaker clusters and the names provided by the video OCR and using a task-adapted variant of the TF-IDF information retrieval coefficient. These methods were tested on the REPERE dry-run evaluation corpus, containing 3 hours of annotated videos. Our best unsupervised system reaches a F-measure of 70.2% when considering all the speakers, and 81.7% if anchor speakers are left out. By comparison, a mono-modal, supervised speaker identification system with 535 speaker models trained on matching development data and additional TV and radio data only provided a 57.5% F-measure when considering all the speakers and 45.7% without anchor.</div>
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Unsupervised Speaker Identification using Overlaid Texts in TV Broadcast

Unsupervised Speaker Identification using Overlaid Texts in TV Broadcast

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