# Non-Autoregressive End-to-End Neural Modeling for Automatic Pronunciation Error Detection
## 5 Conclusions and Future Work  
In this work, we suggest a self-directing non-autoregressive pronunciation mistake identification model using the dictation and pronunciation model. Our proposed architecture is a combination of a dictation model and a pronunciation model. The proposed architecture used the LASO ASR approach for the dictation model and the Bi-LSTM-based approach for the pronunciation model. We used the LASO ASR approach to create a token sequence without explicit language modeling because token relationships are implicit in voice signals. On this basis, LASO forward propagates token creation in a single pass without beam searching. Additionally, the feed-forward structure makes it possible to execute parallel computing effectively and drastically lower the time cost of inference, which verifies the NAR E2E strategy's practical viability for various prospective CAPT jobs. In future studies, we want to investigate more precise suprasegmental, prosodic, accentual, and acoustic pronunciation phenomena for NAR E2E methods for pronunciation error identification and diagnosis.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Misrecognized Vowels**} \\ \hline
**Models** & **iy** & **ih** & **eh** & **ae** & **ah** & **aa** \\ Target Numbers & 2133 & 3970 & 1260 & 2155 & 5321 & 4874 \\ CNN-RNN-CTC & 1097 & 1256 & 544 & 630 & 2035 & 231 \\ Proposed Model & 1124 & 1487 & 720 & 780 & 2460 & 350 \\ \hline \multicolumn{6}{c}{**Misrecognized Consonants**} \\ \hline
**Models** & **z** & **s** & **sh** & **t** & **dh** & **d** \\ Target Numbers & 1160 & 4822 & 844 & 7833 & 1448 & 2420 \\ CNN-RNN-CTC & 228 & 1457 & 313 & 1280 & 125 & 1067 \\ Proposed Model & 340 & 1456 & 340 & 1500 & 180 & 1288 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of most frequently misrecognized vowel and consonant.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Correct-Pronunciation**} & \multicolumn{6}{c}{**Mispronunciation**} \\ \cline{2-7}
**Model Names** & \multicolumn{3}{c}{**Detection (CD)**} & \multicolumn{3}{c}{**Detection (MD)**} \\ \cline{2-7}  & **PR** & **RE** & **F1** & **PR** & **RE** & **F1** \\ \hline Mask-CTC (w/o PM) & 91.62 & 90.94 & 91.28 & 45.73 & 47.87 & 46.77 \\ Mask-CTC (w/PM) & 91.70 & 90.80 & 91.25 & 45.66 & 48.46 & 47.02 \\ CTC-ATT & 91.04 & 92.24 & 91.73 & 47.55 & 42.94 & 45.13 \\ GOP & 91.97 & 90.98 & 91.47 & 46.99 & 50.15 & 48.52 \\ CNN-RNN-CTC & 93.88 & 79.97 & 86.37 & 34.88 & 67.29 & 45.94 \\ Proposed Model (w/o PM) [L2-ARCTIC] & 91.93 & 92.95 & 91.63 & 47.82 & 48.93 & 48.01 \\ Proposed Model (w/PM) [L2-ARCTIC] & 92.03 & 92.45 & 91.82 & 47.95 & 49.32 & 48.65 \\ Proposed Model (w/o PM) [SpeechOcean] & 93.45 & 92.95 & 92.67 & 55.21 & 50.58 & 52.07 \\ Proposed Model (w/PM) [SpeechOcean] & 94.28 & 93.62 & 93.01 & 55.85 & 52.06 & 51.74 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance evaluation on correct pronunciation and incorrect pronunciation detection.  
**Author Contributions:** Supervision, M.F.M.; conceptualization, M.A.H.W. and M.F.M.; methodology; data curation, M.F.M. and M.A.H.W.; software, M.A.H.W.; validation, M.F.M. and M.A., formal analysis, M.A. and M.F.M.; investigation, M.A.; resources, M.F.M. and M.A.H.W.; writing--original draft preparation, M.A.H.W. and M.A.; writing--review and editing, M.A., M.A.H.W.; visualization M.A.H.W.; project administration, M.F.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was supported by the Deanship of Scientific Research at Prince Sattam Bin Abdulaziz University under the research project #(PSAU-2022/01/19401).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.