Disclosed are embodiments for mapping, with a first trained universal function approximator, the speech representation to predicted phonological feature and phoneme class probabilities; determining expected phonological feature values based on an automatic phonetic segmentation using the expected phoneme sequence and the predicted phoneme class probabilities; and classifying, with a second trained universal function approximator different from the first trained universal function approximator, a combination of the predicted phonological feature probabilities and the expected phonological feature values to thereby detect a mispronunciation present in the sampled speech waveform and facilitate phonological feature feedback associated with the mispronunciation.
MISPRONUNCIATION DETECTION WITH PHONOLOGICAL FEEDBACK