# Non-Autoregressive End-to-End Neural Modeling for Automatic Pronunciation Error Detection
## 2 Literature Review  
Various methods for tackling MDD have been presented during the past decade [15; 16; 17; 18]. \"Data sparsity\" is a significant technological difficulty for these models. Specifically, MDD needs a granular diagnosis of pronunciation quality. Hence, manual annotation at the phoneme level for L2 speech is frequently necessary for training these models [18]. Such annotation efforts are very labor intensive, and time consuming [19; 20]. In the extant research, there are two orthogonal approaches to addressing the sparsity of L2 data. One approach is to reduce the MDD model's reliance on L2 training data by employing poorly supervised [21] or unsupervised [22] learning approaches. This research focuses on producing fresh L2 training examples for data augmentation [23; 24; 25; 26]. A naive method for augmenting L2 data is automatically transcribing existing L2 speech using forced-alignment models to generate phoneme annotations [27]. This method, however, does not detect pronunciation problems and results in inaccurate annotations for non-native speech. Obtaining further L2 speech is also challenging, resulting in a relatively restricted corpus of annotated L2 data [28]. A random sampling of L2 phoneme substitutions [23] or sampling based on the probability distribution of phonemes [24] can be used to gather knowledge from real mispronunciation patterns. Phoneme-to-phoneme (P2P) models may also produce mispronounced speech by perturbing the phoneme sequence of the associated L1 speech using decision trees [29] or deep learning approaches [25; 30]. More recently, [PERSON] et al. [26] developed the text-to-speech (T2S) and speech-to-speech (S2S) models as two novel L2 data synthesis strategies. The proposed L2-GEN is a new data augmentation system to produce L2 phoneme sequences and related speech that captures mispronunciation patterns from non-native ESL learners in the real world. L2-GEN presents a machine learning-based L1-to-L2 phoneme paraphrasing model to generate a collection of L2 phoneme sequences given any L1 reference phonemes. [PERSON] et al. [31] coined the term \"goodness of pronunciation\" (GoP) in 2000. GoP begins by utilizing a forced-alignment approach to align the canonical phonemes with the speech signal. This method seeks to identify the most probable mapping between phonemes and areas of a voice stream. GoP then computes the ratio between the probabilities of the canonical and most probable spoken phonemes. Finally, a mispronunciation is identified if the ratio falls below a certain threshold. Deep neural networks (DNNs) [32; 33; 34] were added to GoP to replace the hidden Markov model (HMM) and Gaussian mixture model (GMM) approaches in audio modeling [1; 15]. [PERSON] et al. [35] increased the performance of GoP using an unsupervised latent representation of extracted speech. The paper [36] presents the discovery and solution of mispronunciation using a specifically tailored end-to-end-based ASR structural model that incorporates a hybrid CTC/attention method [37; 38; 39; 40]. In addition, Ref. [36] employed an anti-phone collection to create additional voice training