  In the following report we propose pipelines for Goodness of Pronunciation
(GoP) computation solving OOV problem at testing time using Vocab/Lexicon
expansion techniques. The pipeline uses different components of ASR system to
quantify accent and automatically evaluate them as scores. We use the
posteriors of an ASR model trained on native English speech, along with the
phone level boundaries to obtain phone level pronunciation scores. We used this
as a baseline pipeline and implemented methods to remove UNK and SPN phonemes
in the GoP output by building three pipelines. The Online, Offline and Hybrid
pipeline which returns the scores but also can prevent unknown words in the
final output. The Online method is based per utterance, Offline method
pre-incorporates a set of OOV words for a given data set and the Hybrid method
combines the above two ideas to expand the lexicon as well work per utterance.
We further provide utilities such as the Phoneme to posterior mappings, GoP
scores of each utterance as a vector, and Word boundaries used in the GoP
pipeline for use in future research.
