Methods for foreign accent conversion (FAC) aim to generate speech that sounds similar to a given non-native speaker but with the accent of a native speaker. Conventional FAC methods borrow excitation information (F0 and aperiodicity; produced by a conventional vocoder) from a reference (i.e., native) utterance during synthesis time. As such, the generated speech retains some aspects of the voice quality of the native speaker. We present a framework for FAC that eliminates the need for

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... nal vocoders (e.g., STRAIGHT, World) and therefore the need to use the native speaker's excitation. Our approach uses an acoustic model trained on a native speech corpus to extract speaker-independent phonetic posteriorgrams (PPGs), and then train a speech synthesizer to map PPGs from the non-native speaker into the corresponding spectral features, which in turn are converted into the audio waveform using a high-quality neural vocoder. At runtime, we drive the synthesizer with the PPG extracted from a native reference utterance. Listening tests show that the proposed system produces speech that sounds more clear, natural, and similar to the non-native speaker compared with a baseline system, while significantly reducing the perceived foreign accent of nonnative utterances.