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Pronunciation is one of the fundamentals of language learning, and it isconsidered a primary factor of spoken language when it comes to anunderstanding and being understood by others. The persistent presence of higherror rates in speech recognition domains resulting from mispronunciationsmotivates us to find alternative techniques for handling mispronunciations. Inthis study, we develop a mispronunciation assessment system that checks thepronunciation of non-native English speakers, identifies the commonlymispronounced phonemes of Italian learners of English, and presents anevaluation of the non-native pronunciation observed in phonetically annotatedspeech corpora. In this work, to detect mispronunciations, we used aphone-based ASR implemented using Kaldi. We used two non-native English labeledcorpora; (i) a corpus of Italian adults contains 5,867 utterances from 46speakers, and (ii) a corpus of Italian children consists of 5,268 utterancesfrom 78 children. Our results show that the selected error model candiscriminate correct sounds from incorrect sounds in both native and nonnativespeech, and therefore can be used to detect pronunciation errors in non-nativespeech. The phone error rates show improvement in using the error languagemodel. The ASR system shows better accuracy after applying the error model onour selected corpora.

Experiments of ASR-based mispronunciation detection for children and adult English learners