  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 classical approach, audio features are usually
extracted from fixed regions of speech such as the syllable nucleus. We propose
an attention-based deep learning model that automatically derives optimal
syllable-level representation from frame-level and phoneme-level audio
features. Training this model is challenging because of the limited amount of
incorrect stress patterns. To solve this problem, we propose to augment the
training set with incorrectly stressed words generated with Neural TTS.
Combining both techniques achieves 94.8% precision and 49.2% recall for the
detection of incorrectly stressed words in L2 English speech of Slavic and
Baltic speakers.
