  Mispronunciation detection is an essential component of the Computer-Assisted
Pronunciation Training (CAPT) systems. State-of-the-art mispronunciation
detection models use Deep Neural Networks (DNN) for acoustic modeling, and a
Goodness of Pronunciation (GOP) based algorithm for pronunciation scoring.
  However, GOP based scoring models have two major limitations: i.e., (i) They
depend on forced alignment which splits the speech into phonetic segments and
independently use them for scoring, which neglects the transitions between
phonemes within the segment;
  (ii) They only focus on phonetic segments, which fails to consider the
context effects across phonemes (such as liaison, omission, incomplete plosive
sound, etc.).
  In this work, we propose the Context-aware Goodness of Pronunciation (CaGOP)
scoring model. Particularly, two factors namely the transition factor and the
duration factor are injected into CaGOP scoring.
  The transition factor identifies the transitions between phonemes and applies
them to weight the frame-wise GOP. Moreover, a self-attention based phonetic
duration modeling is proposed to introduce the duration factor into the scoring
model.
  The proposed scoring model significantly outperforms baselines, achieving 20%
and 12% relative improvement over the GOP model on the phoneme-level and
sentence-level mispronunciation detection respectively.
