| DC 欄位 |
值 |
語言 |
| DC.contributor | 資訊工程學系 | zh_TW |
| DC.creator | 賴泓榮 | zh_TW |
| DC.creator | Hong-Rong Lai | en_US |
| dc.date.accessioned | 2023-8-15T07:39:07Z | |
| dc.date.available | 2023-8-15T07:39:07Z | |
| dc.date.issued | 2023 | |
| dc.identifier.uri | http://ir.lib.ncu.edu.tw:88/thesis/view_etd.asp?URN=108522101 | |
| dc.contributor.department | 資訊工程學系 | 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，深度學習技術在語音識別、自然語言處理等領域取得了顯著的成果。深度學習中的神經網路模型具有一定的泛化能力，通過神經網路在大量數據上的訓練，模型能夠學習到更廣泛的語音變異性，從而具有更好的適應性。它不僅可以提高多語言環境中的語音識別效果，還能夠減少對標記數據的依賴並簡化系統開發流程。通過持續的研究和改進，實現更準確和可靠的多語言識別系統，這項研究對於地區性低資源語言的發展和保護具有重要意義，同時也有助於促進跨文化的交流和理解。 | zh_TW |
| dc.description.abstract | In recent years, deep learning techniques have achieved remarkable progress in speech recognition, natural language processing, and other fields. Neural network models in deep learning demonstrate a certain level of generalization ability. Through training on extensive data, these models can learn a broader range of speech variations, leading to improved adaptability. Deep learning not only enhances speech recognition in multilingual environments but also reduces reliance on annotated data and simplifies system development processes. By continuously researching and improving, achieving more accurate and reliable multilingual recognition systems holds significant importance for the development and preservation of regional low-resource languages. Additionally, it facilitates cross-cultural communication and understanding. | en_US |
| DC.subject | 多語言語音模型 | zh_TW |
| DC.subject | 低資源語言 | zh_TW |
| DC.subject | 語音識別 | zh_TW |
| DC.subject | 深度學習 | zh_TW |
| DC.title | 基於大規模多語言 語音模型於在地化語言實務應用 | zh_TW |
| dc.language.iso | zh-TW | zh-TW |
| DC.title | Localization Language Applications Based on Large-scale Multilingual Speech Models | en_US |
| DC.type | 博碩士論文 | zh_TW |
| DC.type | thesis | en_US |
| DC.publisher | National Central University | en_US |