Abstract
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.
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.
Original language | English |
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Title of host publication | Proc. Interspeech 2022 |
Place of Publication | Korea |
Pages | 1208-1212 |
Number of pages | 5 |
DOIs | |
State | Published - 2022 |