A sequence-to-sequence voice conversion model trained on a native rater's shadowing utterances can spot unintelligible segments in L2 speech, beating an ASR baseline on the native rater but not on all listeners.
The first approach leverages alignment failure to mimic rater perception break- downs, thereby identifying segments of unintelligibility
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A Perception-Based L2 Speech Intelligibility Indicator: Leveraging a Rater's Shadowing and Sequence-to-sequence Voice Conversion
A sequence-to-sequence voice conversion model trained on a native rater's shadowing utterances can spot unintelligible segments in L2 speech, beating an ASR baseline on the native rater but not on all listeners.