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Computer-assisted Pronunciation Training -- Speech synthesis is almost all you need
We show that these techniques not only improve the accuracy of three machine learning models for detecting pronunciation errors but also help establish a new state-of-the-art in the field.
Weakly-supervised word-level pronunciation error detection in non-native English speech
To train this model, phonetically transcribed L2 speech is not required and we only need to mark mispronounced words.
Mispronunciation Detection in Non-native (L2) English with Uncertainty Modeling
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.
Detection of Lexical Stress Errors in Non-Native (L2) English with Data Augmentation and Attention
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).
Interpretable Deep Learning Model for the Detection and Reconstruction of Dysarthric Speech
This paper proposed a novel approach for the detection and reconstruction of dysarthric speech.
