  This report proposes state-of-the-art research in the field of Computer
Assisted Language Learning (CALL). Mispronunciation detection is one of the
core components of Computer Assisted Pronunciation Training (CAPT) systems
which is a subset of CALL. Studies on automated pronunciation error detection
began in the 1990s, but the development of fullfledged CAPTs has only
accelerated in the last decade due to an increase in computing power and
availability of mobile devices for recording speech required for pronunciation
analysis. Detecting Pronunciation errors is a hard problem to solve as there is
no formal definition of correct and incorrect pronunciation. As a result,
typically prosodic and phoneme errors such as phoneme substitution, insertion,
and deletion are detected. Also, it has been agreed upon that learning
pronunciation should focus on speaker intelligibility rather than sounding like
an L1 English speaker. Initially, methods were developed on posterior
likelihood called Good of Pronunciation using Gaussian Mixture Model-Hidden
Markov Model and Deep Neural Network-Hidden Markov Model approaches. These are
complex systems to implement when compared with the recently proposed ASR based
End-to-End mispronunciations detection systems. The purpose of this research is
to create End-to-End (E2E) models using Connectionist Temporal Classification
(CTC) and Attention-based sequence decoder. Recently, E2E models have shown
considerable improvement in mispronunciation detection accuracy. This research
will draw comparison amongst baseline models CNN-RNN-CTC, CNN-RNN-CTC with
character sequence-based attention decoder, and CNN-RNN-CTC with phoneme-based
decoder systems. This study will help us in deciding a better approach towards
developing an efficient mispronunciation detection system.
