Mispronunciation Detection and Diagnosis in L2 English Speech Using Multidistribution Deep Neural Networks

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Abstract

This paper investigates the use of multidistribution deep neural networks (DNNs) for mispronunciation detection and diagnosis (MDD), to circumvent the difficulties encountered in an existing approach based on extended recognition networks (ERNs). The ERNs leverage existing automatic speech recognition technology by constraining the search space via including the likely phonetic error patterns of the target words in addition to the canonical transcriptions. MDDs are achieved by comparing the recognized transcriptions with the canonical ones. Although this approach performs reasonably well, it has the following issues: 1) Learning the error patterns of the target words to generate the ERNs remains a challenging task. Phones or phone errors missing from the ERNs cannot be recognized even if we have well-trained acoustic models; and 2) acoustic models and phonological rules are trained independently, and hence, contextual information is lost. To address these issues, we propose an acoustic-graphemic-phonemic model (AGPM) using a multidistribution DNN, whose input features include acoustic features, as well as corresponding graphemes and canonical transcriptions (encoded as binary vectors). The AGPM can implicitly model both grapheme-to-likely-pronunciation and phoneme-to-likely-pronunciation conversions, which are integrated into acoustic modeling. With the AGPM, we develop a unified MDD framework, which works much like free-phone recognition. Experiments show that our method achieves a phone error rate (PER) of 11.1%. The false rejection rate (FRR), false acceptance rate (FAR), and diagnostic error rate (DER) for MDD are 4.6%, 30.5%, and 13.5%, respectively. It outperforms the ERN approach using DNNs as acoustic models, whose PER, FRR, FAR, and DER are 16.8%, 11.0%, 43.6%, and 32.3%, respectively.

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