Speaker diarization, also known as the "who spoke when?" task, 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 the dialogue scenes of TV series by combining the audio and video modalities: speaker diarization is first performed by using each of these modalities; the two resulting partitions of the instance set are then optimally matched, before the remaining instances, corresponding to cases of disagreement between the modalities, are finally processed. The results obtained by applying such a multi-modal approach to fictional films turn out to outperform those obtained by using a single modality.
REF presents a recent approach on speaker diarization of TV series exploiting both audio and video data.
Audiovisual speaker diarization of TV series
2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Apr 2015, Brisbane, Australia. 2015
Computer Science