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

Pronunciation is one of the fundamentals of language learning, and it is considered a primary factor of spoken language when it
comes to an understanding and being understood by others. The persistent presence of high error rates in speech recognition domains resulting from mispronunciations motivates us to find alternative techniques
for handling mispronunciations. In this study, we develop a mispronunciation assessment system that checks the pronunciation of non-native
English speakers, identifies the commonly mispronounced phonemes of
Italian learners of English, and presents an evaluation of the non-native
pronunciation observed in phonetically annotated speech corpora. In this
work, to detect mispronunciations, we used a phone-based ASR implemented using Kaldi. We used two non-native English labeled corpora;
(i) a corpus of Italian adults contains 5,867 utterances from 46 speakers,
and (ii) a corpus of Italian children consists of 5,268 utterances from 78
children. Our results show that the selected error model can discriminate correct sounds from incorrect sounds in both native and non-native
speech, and therefore can be used to detect pronunciation errors in nonnative speech. The phone error rates show improvement in using the
error language model. Furthermore, the ASR system shows better accuracy after applying the error model on our selected corpora.