  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 nonnative
speech, and therefore can be used to detect pronunciation errors in non-native
speech. The phone error rates show improvement in using the error language
model. The ASR system shows better accuracy after applying the error model on
our selected corpora.
