39 citations · 40 across the 5 of their papers we have counts for
5 papers
Computer-assisted Pronunciation Training -- Speech synthesis is almost all you need
Daniel Korzekwa, Jaime Lorenzo-Trueba, Thomas Drugman +1
The research community has long studied computer-assisted pronunciation training (CAPT) methods in non-native speech. Researchers focused on studying various model architectures, s…
Weakly-supervised word-level pronunciation error detection in non-native English speech
Daniel Korzekwa, Jaime Lorenzo-Trueba, Thomas Drugman +2
We propose a weakly-supervised model for word-level mispronunciation detection in non-native (L2) English speech. To train this model, phonetically transcribed L2 speech is not req…
Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling
Daniel Korzekwa, Jaime Lorenzo-Trueba, Szymon Zaporowski +3
A common approach to the automatic detection of mispronunciation in language learning is to recognize the phonemes produced by a student and compare it to the expected pronunciatio…
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
Daniel Korzekwa, Roberto Barra-Chicote, Szymon Zaporowski +6
This paper describes two novel complementary techniques that improve the detection of lexical stress errors in non-native (L2) English speech: attention-based feature extraction an…
Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
Daniel Korzekwa, Roberto Barra-Chicote, Bozena Kostek +2
This paper proposed a novel approach for the detection and reconstruction of dysarthric speech. The encoder-decoder model factorizes speech into a low-dimensional latent space and…