44 citations · 100 across the 34 of their papers we have counts for
6 papers · 2 filters
Speech-based emotion recognition with self-supervised models using attentive channel-wise correlations and label smoothing
Sofoklis Kakouros, Themos Stafylakis, Ladislav Mosner +1
When recognizing emotions from speech, we encounter two common problems: how to optimally capture emotion-relevant information from the speech signal and how to best quantify or ca…
Multi-Speaker and Wide-Band Simulated Conversations as Training Data for End-to-End Neural Diarization
Federico Landini, Mireia Diez, Alicia Lozano-Diez +1
End-to-end diarization presents an attractive alternative to standard cascaded diarization systems because a single system can handle all aspects of the task at once. Many flavors…
Extracting speaker and emotion information from self-supervised speech models via channel-wise correlations
Themos Stafylakis, Ladislav Mosner, Sofoklis Kakouros +3
Self-supervised learning of speech representations from large amounts of unlabeled data has enabled state-of-the-art results in several speech processing tasks. Aggregating these s…
An attention-based backend allowing efficient fine-tuning of transformer models for speaker verification
Junyi Peng, Oldrich Plchot, Themos Stafylakis +3
In recent years, self-supervised learning paradigm has received extensive attention due to its great success in various down-stream tasks. However, the fine-tuning strategies for a…
Analyzing speaker verification embedding extractors and back-ends under language and channel mismatch
Anna Silnova, Themos Stafylakis, Ladislav Mosner +6
In this paper, we analyze the behavior and performance of speaker embeddings and the back-end scoring model under domain and language mismatch. We present our findings regarding Re…
DPCCN: Densely-Connected Pyramid Complex Convolutional Network for Robust Speech Separation And Extraction
Jiangyu Han, Yanhua Long, Lukas Burget +1
In recent years, a number of time-domain speech separation methods have been proposed. However, most of them are very sensitive to the environments and wide domain coverage tasks.…