Anomaly detection with a variational autoencoder for Arabic mispronunciation detection
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10772-024-10113-9.pdf
Reference28 articles.
1. Aggarwal, C. C. (2017). Outlier analysis. Springer. https://doi.org/10.1007/978-3-319-47578-3
2. Ahmed, A., Bader, M., Shahin, I., Nassif, A. B., Werghi, N., & Basel, M. (2023). Arabic mispronunciation recognition system using LSTM network. Information, 14(7), 413. https://doi.org/10.3390/info14070413
3. Akhtar, S., Hussain, F., Raja, F. R., Ehatisham-ul-haq, M., Baloch, N. K., Ishmanov, F., & Zikria, Y. B. (2020). Improving mispronunciation detection of Arabic words for non-native learners using deep convolutional neural network features. Electronics, 9(6), 963.
4. Algabri, M., Mathkour, H., Alsulaiman, M., & Bencherif, M. A. (2022). Mispronunciation detection and diagnosis with articulatory-level feedback generation for non-native Arabic speech. Mathematics, 10(15), 2727. https://doi.org/10.3390/math10152727
5. Aly, S. A., Salah, A., & Eraqi, H. M. (2021). ASMDD : Arabic speech mispronunciation detection dataset. arXiv preprint. https://doi.org/10.48550/arXiv.2111.01136
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