Neural speech turn segmentation and affinity propagation for speaker diarization - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année : 2018

Neural speech turn segmentation and affinity propagation for speaker diarization

Résumé

Speaker diarization is the task of determining "who speaks when" in an audio stream. Most diarization systems rely on statistical models to address four sub-tasks: speech activity detection (SAD), speaker change detection (SCD), speech turn clustering, and re-segmentation. First, following the recent success of recurrent neural networks (RNN) for SAD and SCD, we propose to address re-segmentation with Long-Short Term Memory (LSTM) networks. Then, we propose to use affinity propagation on top of neural speaker embeddings for speech turn clustering, outperforming regular Hierarchical Agglomerative Clustering (HAC). Finally, all these modules are combined and jointly optimized to form a speaker diarization pipeline in which all but the clustering step are based on RNNs. We provide experimental results on the French Broadcast dataset ETAPE where we reach state-of-the-art performance.
Fichier principal
Vignette du fichier
neural-speech-turn(2).pdf (225.93 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01912236 , version 1 (08-11-2018)

Identifiants

  • HAL Id : hal-01912236 , version 1

Citer

Ruiqing Yin, Hervé Bredin, Claude Barras. Neural speech turn segmentation and affinity propagation for speaker diarization. Annual Conference of the International Speech Communication Association, Sep 2018, Hyderabad, India. ⟨hal-01912236⟩
878 Consultations
1144 Téléchargements

Partager

Gmail Facebook X LinkedIn More