22 citations · 28 across the 9 of their papers we have counts for
24 papers
Improving Speaker Verification with Self-Pretrained Transformer Models
Junyi Peng, Oldřich Plchot, Themos Stafylakis +3
Recently, fine-tuning large pre-trained Transformer models using downstream datasets has received a rising interest. Despite their success, it is still challenging to disentangle t…
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…
Parameter-efficient transfer learning of pre-trained Transformer models for speaker verification using adapters
Junyi Peng, Themos Stafylakis, Rongzhi Gu +4
Recently, the pre-trained Transformer models have received a rising interest in the field of speech processing thanks to their great success in various downstream tasks. However, m…
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…
On the Use of Semantically-Aligned Speech Representations for Spoken Language Understanding
Gaëlle Laperrière, Valentin Pelloin, Mickaël Rouvier +2
In this paper we examine the use of semantically-aligned speech representations for end-to-end spoken language understanding (SLU). We employ the recently-introduced SAMU-XLSR mode…
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…