no code implementations • 16 Jan 2024 • Jenthe Thienpondt, Kris Demuynck
In this paper, we present ECAPA2, a novel hybrid neural network architecture and training strategy to produce robust speaker embeddings.
no code implementations • 27 Nov 2023 • François Remy, Kris Demuynck, Thomas Demeester
Our new multilingual model enables a range of languages to benefit from our advancements in biomedical semantic representation learning, opening a new avenue for bioinformatics researchers around the world.
no code implementations • 5 Oct 2023 • François Remy, Pieter Delobelle, Bettina Berendt, Kris Demuynck, Thomas Demeester
This one-to-many token mapping improves tremendously the initialization of the embedding table for the target language.
no code implementations • 10 Jul 2023 • Jenthe Thienpondt, Caroline M. Speksnijder, Kris Demuynck
In this paper, we analyze the behavior of speaker embeddings of patients during oral cancer treatment.
no code implementations • 7 Apr 2023 • Jenthe Thienpondt, Nilesh Madhu, Kris Demuynck
Most speaker verification systems are designed with the assumption of a single speaker being present in a given audio segment.
no code implementations • 20 Nov 2022 • Anup Singh, Kris Demuynck, Vipul Arora
These systems deploy indexing methods, which quantize fingerprints to hash codes in an unsupervised manner to expedite the search.
no code implementations • 21 Oct 2022 • François Remy, Kris Demuynck, Thomas Demeester
This work introduces BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts.
no code implementations • 16 Oct 2022 • Anup Singh, Kris Demuynck, Vipul Arora
An ideal audio retrieval system efficiently and robustly recognizes a short query snippet from an extensive database.
no code implementations • 19 Jun 2022 • Jenthe Thienpondt, Kris Demuynck
This can mainly be attributed to the absence of large children's speech corpora to train robust ASR models and the resulting domain mismatch when decoding children's speech with systems trained on adult data.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +3
no code implementations • 18 Oct 2021 • Jenthe Thienpondt, Brecht Desplanques, Kris Demuynck
This paper contains a post-challenge performance analysis on cross-lingual speaker verification of the IDLab submission to the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21).
no code implementations • 9 Sep 2021 • Jenthe Thienpondt, Brecht Desplanques, Kris Demuynck
The final system fusion with two ECAPA CNN-TDNNs and three SE-ResNets enhanced with frequency positional information achieved a third place on the VoxSRC-21 leaderboard for both track 1 and 2 with a minDCF of 0. 1291 and 0. 1313 respectively.
no code implementations • 2 Aug 2021 • Siyuan Song, Brecht Desplanques, Celest De Moor, Kris Demuynck, Nilesh Madhu
We show that the use of noise-floor features is complementary to multi-condition training in which foreground speech is added to training signal to reduce the mismatch between training and testing conditions.
no code implementations • 6 Apr 2021 • Jenthe Thienpondt, Brecht Desplanques, Kris Demuynck
These learnable feature map biases along the frequency axis offer this architecture a straightforward way to exploit frequency positional information.
no code implementations • 15 Jul 2020 • Jenthe Thienpondt, Brecht Desplanques, Kris Demuynck
In this paper we describe the top-scoring IDLab submission for the text-independent task of the Short-duration Speaker Verification (SdSV) Challenge 2020.
1 code implementation • EMNLP 2018 • Fréderic Godin, Kris Demuynck, Joni Dambre, Wesley De Neve, Thomas Demeester
In this paper, we investigate which character-level patterns neural networks learn and if those patterns coincide with manually-defined word segmentations and annotations.
1 code implementation • LREC 2016 • Joris Pelemans, Lyan Verwimp, Kris Demuynck, Hugo Van hamme, Patrick Wambacq
In this paper we present SCALE, a new Python toolkit that contains two extensions to n-gram language models.
no code implementations • LREC 2014 • Joris Pelemans, Kris Demuynck, Hugo Van hamme, Patrick Wambacq
In this paper we present 3 applications in the domain of Automatic Speech Recognition for Dutch, all of which are developed using our in-house speech recognition toolkit SPRAAK.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1