Mandarin Chinese Mispronunciation Detection and Diagnosis Leveraging Deep Neural Network Based Acoustic Modeling and Training Techniques

Chen, Berlin; Hsu, Yao Chi

doi:10.1007/978-981-13-3570-9_11

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…ASR has active research attention in different human languages [11]. In addition to ASR, many studies performed speech recognition with mispronunciation detection for children and other non-native language learners in many languages, i.e., English [12][13][14], and Mandarin Chinese [15,16]. However, limited work has been done in Arabic ASR using pattern recognition and feature extraction techniques.…”

Section: Introductionmentioning

confidence: 99%

Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning

et al. 2021

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Automatic speech recognition for Arabic has its unique challenges and there has been relatively slow progress in this domain. Specifically, Classic Arabic has received even less research attention. The correct pronunciation of the Arabic alphabet has significant implications on the meaning of words. In this work, we have designed learning models for the Arabic alphabet classification based on the correct pronunciation of an alphabet. The correct pronunciation classification of the Arabic alphabet is a challenging task for the research community. We divide the problem into two steps, firstly we train the model to recognize an alphabet, namely Arabic alphabet classification. Secondly, we train the model to determine its quality of pronunciation, namely Arabic alphabet pronunciation classification. Due to the less availability of audio data of this kind, we had to collect audio data from the experts, and novices for our model’s training. To train these models, we extract pronunciation features from audio data of the Arabic alphabet using mel-spectrogram. We have employed a deep convolution neural network (DCNN), AlexNet with transfer learning, and bidirectional long short-term memory (BLSTM), a type of recurrent neural network (RNN), for the classification of the audio data. For alphabet classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 95.95%, 98.41%, and 88.32%, respectively. For Arabic alphabet pronunciation classification, DCNN, AlexNet, and BLSTM achieve an accuracy of 97.88%, 99.14%, and 77.71%, respectively.

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Section: Introductionmentioning

confidence: 99%

Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning

et al. 2021

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Mispronunciation detection and diagnosis using deep neural networks: a systematic review

Lounis,

Dendani,

Bahi

2024

Multimed Tools Appl

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Regularized Deep LSTM Autoencoder for Phonological Deviation Assessment

Pravin¹,

Palanivelan²

2020

Int. J. Patt. Recogn. Artif. Intell.

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In this paper, the Deep Long-short term memory Autoencoder (DLAE), a regularized deep learning model, is proposed for the automatic severity assessment of phonological deviations which are crucial stuttering markers in children. This automatic noninvasive severity assessment plays a paramount role in prevenient diagnosis, progress inference, and post-care for the patients with specific speech disorder. The proposed model is an implementation of a multi-layered Autoencoder in the Encoder–Decoder architecture of the Long-Short Term Memory (LSTM) model with hierarchically appended hidden layers and hidden units. The DLAE has definite advantage over the baseline Autoencoders. During the training phase, the proposed DLAE reconstructs the phonological features in an unsupervised fashion and the latent bottleneck features are extracted from the Encoder. The trained and regularized DLAE model with drop out is then used to predict the severity of the phonological deviation with high precision and classification accuracy compared to the baseline models.

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Mandarin Chinese Mispronunciation Detection and Diagnosis Leveraging Deep Neural Network Based Acoustic Modeling and Training Techniques

Cited by 3 publications

References 31 publications

Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning

Correct Pronunciation Detection of the Arabic Alphabet Using Deep Learning

Mispronunciation detection and diagnosis using deep neural networks: a systematic review

Regularized Deep LSTM Autoencoder for Phonological Deviation Assessment

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