activity
20172024
most citedConvolutional Neural Networks and x-vector Embedding for DCASE2018 Acoustic Scene Classification Challenge

44 citations · 100 across the 34 of their papers we have counts for

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Showing 2022Show all

8 papers · 1 filter

eess.AS2022

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…

eess.AS2022★ 1 cited

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…

cs.SD2022

Toroidal Probabilistic Spherical Discriminant Analysis

Anna Silnova, Niko Brümmer, Albert Swart +1

In speaker recognition, where speech segments are mapped to embeddings on the unit hypersphere, two scoring back-ends are commonly used, namely cosine scoring and PLDA. We have rec…

eess.AS2022★ 1 cited

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…

eess.AS2022★ 1 cited

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…

cs.SD2022

Speaker adaptation for Wav2vec2 based dysarthric ASR

Murali Karthick Baskar, Tim Herzig, Diana Nguyen +4

Dysarthric speech recognition has posed major challenges due to lack of training data and heavy mismatch in speaker characteristics. Recent ASR systems have benefited from readily…