Audiovisual speaker diarization of TV series
This addresses the problem of accurately identifying speakers in narrative films for media analysis, though it is incremental as it builds on existing multimodal techniques.
The paper tackled speaker diarization in TV series by combining audio and video modalities to handle adverse acoustic conditions like background music and sound effects, resulting in improved performance over single-modality approaches.
Speaker diarization may be difficult to achieve when applied to narrative films, where speakers usually talk in adverse acoustic conditions: background music, sound effects, wide variations in intonation may hide the inter-speaker variability and make audio-based speaker diarization approaches error prone. On the other hand, such fictional movies exhibit strong regularities at the image level, particularly within dialogue scenes. In this paper, we propose to perform speaker diarization within dialogue scenes of TV series by combining the audio and video modalities: speaker diarization is first performed by using each modality, the two resulting partitions of the instance set are then optimally matched, before the remaining instances, corresponding to cases of disagreement between both modalities, are finally processed. The results obtained by applying such a multi-modal approach to fictional films turn out to outperform those obtained by relying on a single modality.