# Non-Autoregressive End-to-End Neural Modeling for Automatic Pronunciation Error Detection
###### Abstract  
A crucial element of computer-assisted pronunciation training systems (CAPT) is the mispronunciation detection and diagnostic (MDD) technique. The provided transcriptions can act as a teacher when evaluating the pronunciation quality of finite speech. The preceding texts have been entirely employed by conventional approaches, such as forced alignment and extended recognition networks, for model development or for enhancing system performance. The incorporation of earlier texts into model training has recently been attempted using end-to-end (E2E)-based approaches, and preliminary results indicate efficacy. Attention-based end-to-end models have shown lower speech recognition performance because multi-pass left-to-right forward computation constrains their practical applicability in beam search. In addition, end-to-end neural approaches are typically data-hungry, and a lack of non-native training data will frequently impair their effectiveness in MDD. To solve this problem, we provide a unique MDD technique that uses non-autoregressive (NAR) end-to-end neural models to greatly reduce estimation time while maintaining accuracy levels similar to traditional E2E neural models. In contrast, NAR models can generate parallel token sequences by accepting parallel inputs instead of left-to-right forward computation. To further enhance the effectiveness of MDD, we develop and construct a pronunciation model superimposed on our approach's NAR end-to-end models. To test the effectiveness of our strategy against some of the best end-to-end models, we use publicly accessible L2-ARCTIC and SpeechOcean English datasets for training and testing purposes where the proposed model shows the best results than other existing models. non-autoregressive; pronunciation modeling; speech recognition; mispronunciation detection and diagnosis; attention; computer-assisted pronunciation training (CAPT) 202202  
Modern MDD pipelines are complex. Extended recognition networks (ERNs) were utilized by [3; 4; 5] to identify both standard phonemes and mispronunciations. Acoustic Phonological Model (APM) developed in [1] utilized multi-distribution deep neural network (DNN) to forecast the most probable phonetic mispronunciations. To further enhance the performance of APM models, the articulatory acoustic-phonemic model (AAPM) [6] and multi-task APM (MT-APM) [7] were developed. Although these methods are viable, they have several drawbacks: (1) They frequently comprise several laborious-to-design phases. For example, it is challenging to construct ERNs [3; 4; 5] because we cannot locate all phonological rules and the rules themselves are difficult to develop. (2) Because the components of these techniques are taught individually, mistakes may accumulate. (3) When constructing a new system, the intricacy of contemporary MDD processes necessitates large engineering work. Thus, a combined end-to-end (E2E) MDD model has several benefits. First, this solution eliminates the requirement for phonological rules and forced alignments, which may need fragile design decisions. Second, it facilitates richer conditioning on traits such as L1 (mother tongue) languages. This is due to the fact that conditioning may take place throughout the whole model, not just on particular elements. Consequently, adaptation with updated information may also be simplified. Lastly, an individual system is more likely to be more resilient than a multi-stage model in which the faults of each component might accumulate. Researchers have recently attempted to develop an E2E method for CAPT systems. Ref. [8] suggested an approach for E2E scoring that employs both acoustic and lexical data. However, their model's output is a basic score that does not include mispronunciation diagnosis. CNN-RNN-CTC [9] is, to our knowledge, the first end-to-end model presented for MDD. It is effective on the CU-CHLOE [10] corpus. However, not all of the provided information is utilized.