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pro vyhledávání: '"Kostek, Bożena"'
The research community has long studied computer-assisted pronunciation training (CAPT) methods in non-native speech. Researchers focused on studying various model architectures, such as Bayesian networks and deep learning methods, as well as on the
Externí odkaz:
http://arxiv.org/abs/2207.00774
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 required and we only need to mark mispronounced words. The lack of phonet
Externí odkaz:
http://arxiv.org/abs/2106.03494
Autor:
Korzekwa, Daniel, Lorenzo-Trueba, Jaime, Zaporowski, Szymon, Calamaro, Shira, Drugman, Thomas, Kostek, Bozena
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 pronunciation of a native speaker. This approach makes two simplifying assumptions
Externí odkaz:
http://arxiv.org/abs/2101.06396
Publikováno v:
International Journal of Applied Mathematics and Computer Science, Vol 33, Iss 3, Pp 479-492 (2023)
The Lombard effect is an involuntary increase in the speaker’s pitch, intensity, and duration in the presence of noise. It makes it possible to communicate in noisy environments more effectively. This study aims to investigate an efficient method f
Externí odkaz:
https://doaj.org/article/e4d036b1a7134a26bebe6ba2c6dfb398
Autor:
Korzekwa, Daniel, Barra-Chicote, Roberto, Zaporowski, Szymon, Beringer, Grzegorz, Lorenzo-Trueba, Jaime, Serafinowicz, Alicja, Droppo, Jasha, Drugman, Thomas, Kostek, Bozena
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 and data augmentation based on Neural Text-To-Speech (TTS). In a classic
Externí odkaz:
http://arxiv.org/abs/2012.14788
Autor:
Korzekwa, Daniel, Barra-Chicote, Roberto, Kostek, Bozena, Drugman, Thomas, Lajszczak, Mateusz
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 encoding of the input text. We showed that the latent space conveys in
Externí odkaz:
http://arxiv.org/abs/1907.04743
Publikováno v:
In Procedia Computer Science 2023 225:1019-1027
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