  Learning a new language involves constantly comparing speech productions with
reference productions from the environment. Early in speech acquisition,
children make articulatory adjustments to match their caregivers' speech.
Grownup learners of a language tweak their speech to match the tutor reference.
This paper proposes a method to synthetically generate correct pronunciation
feedback given incorrect production. Furthermore, our aim is to generate the
corrected production while maintaining the speaker's original voice.
  The system prompts the user to pronounce a phrase. The speech is recorded,
and the samples associated with the inaccurate phoneme are masked with zeros.
This waveform serves as an input to a speech generator, implemented as a deep
learning inpainting system with a U-net architecture, and trained to output a
reconstructed speech. The training set is composed of unimpaired proper speech
examples, and the generator is trained to reconstruct the original proper
speech. We evaluated the performance of our system on phoneme replacement of
minimal pair words of English as well as on children with pronunciation
disorders. Results suggest that human listeners slightly prefer our generated
speech over a smoothed replacement of the inaccurate phoneme with a production
of a different speaker.
