| DC 欄位 |
值 |
語言 |
| DC.contributor | 通訊工程學系 | zh_TW |
| DC.creator | 楊東翰 | zh_TW |
| DC.creator | Dong-Han Yang | en_US |
| dc.date.accessioned | 2022-1-25T07:39:07Z | |
| dc.date.available | 2022-1-25T07:39:07Z | |
| dc.date.issued | 2022 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=106523601 | |
| dc.contributor.department | 通訊工程學系 | zh_TW |
| DC.description | 國立中央大學 | zh_TW |
| DC.description | National Central University | en_US |
| dc.description.abstract | 近年來隨著資訊科技化，人工智慧逐漸深入了我們的生活。深度學習的發展更讓語音辨識技術向前邁進了一大步，不僅能提高人機交互性，還可以應用於醫療等方面。我們用基於深度學習的語音識別技術進行錯誤發音的檢測，以此幫助有構音異常的人找出發音錯誤的地方以增加口說熟練度，並且輔助醫師進行診斷與治療。
本論文「基於卷積遞迴神經網路之構音異常評估技術」，延續過去學者的研究，提出基於CRNN-CTC 改善的系統，來提升錯誤發音檢測 (Mispronunciation Detection, MD) 的效果，達到構音異常的評估。本研究利用卷積遞迴神經網路 (Convolutional Recurrent Neural Network, CRNN) 與連結時序分類 (Connectionist Temporal Classification, CTC) 來訓練網路模型。並加入注意力機制，對構音異常評估的性能進行改善，以提升評估效果。實驗結果表明該方法用於構音異常的檢測有著良好效果。 | zh_TW |
| dc.description.abstract | In recent years, with the advancement of Information Technology, artificial intelligence has gradually penetrated into our lives. The development of deep learning has made speech recognition technology a big step forward, not only can improve human-computer interaction, but also can be applied to medical treatment and other aspects.
In this paper, continuing the research of past scholars, we propose a system which is based on improved CRNN-CTC algorithm that can improve the effect of mispronunciation detection and achieve the evaluation of Articulation Dis-orders. We use Convolutional Recurrent Neural Network (CRNN) and Con-nectionist Temporal Classification (CTC) with attention model to train the model. The experimental results show that this method has a good effect in the auto-matic evaluation of abnormal articulation. | en_US |
| DC.subject | 深度學習 | zh_TW |
| DC.subject | 語音辨識 | zh_TW |
| DC.subject | 構音異常 | zh_TW |
| DC.subject | 卷積遞迴神經網路 | zh_TW |
| DC.subject | 錯誤發音檢測與診斷 | zh_TW |
| DC.subject | Deep Learning | en_US |
| DC.subject | Automatic Speech Recognition | en_US |
| DC.subject | Articulation Disorders | en_US |
| DC.subject | Convolutional Recurrent Neural Network | en_US |
| DC.subject | Mispronunciation Detection and Diagnosis | en_US |
| DC.title | 基於卷積遞迴神經網路之構音異常評估技術 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Automatic Evaluation of Articulation Disorders Based on Convolutional Recurrent Neural Network | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |