Abstract:Phone-level pronunciation scoring is a challenging task, with performance far from that of human annotators. Standard systems generate a score for each phone in a phrase using models trained for automatic speech recognition (ASR) with native data only. Better performance has been shown when using systems that are trained specifically for the task using nonnative data. Yet, such systems face the challenge that datasets labelled for this task are scarce and usually small. In this paper, we present a transfer lea… Show more
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