Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling

no code implementations • 16 Jan 2021 • Daniel Korzekwa, Jaime Lorenzo-Trueba, Szymon Zaporowski, Shira Calamaro, Thomas Drugman, Bozena Kostek

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.

Automatic Phoneme Recognition Sentence +1

Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention

no code implementations • 29 Dec 2020 • Daniel Korzekwa, Roberto Barra-Chicote, Szymon Zaporowski, Grzegorz Beringer, Jaime Lorenzo-Trueba, Alicja Serafinowicz, Jasha Droppo, Thomas Drugman, Bozena Kostek

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).

Data Augmentation