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End-to-end (E2E) neural models are increasingly attracting attention as apromising modeling approach for mispronunciation detection and diagnosis (MDD).Typically, these models are trained by optimizing a cross-entropy criterion,which corresponds to improving the log-likelihood of the training data.However, there is a discrepancy between the objectives of model training andthe MDD evaluation, since the performance of an MDD model is commonly evaluatedin terms of F1-score instead of phone or word error rate (PER/WER). In view ofthis, we in this paper explore the use of a discriminative objective functionfor training E2E MDD models, which aims to maximize the expected F1-scoredirectly. A series of experiments conducted on the L2-ARCTIC dataset show thatour proposed method can yield considerable performance improvements in relationto some state-of-the-art E2E MDD approaches and the celebrated GOP method.

Maximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech