博碩士論文 109552029 詳細資訊




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姓名 鄭雅馨(Ya-Hsin Cheng)  查詢紙本館藏   畢業系所 資訊工程學系在職專班
論文名稱 基於深度學習的語音人名辨識系統
(Deep Learning Based Speech Personal Name Recognition System)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2027-6-25以後開放)
摘要(中) 公司總機人員經常需要轉接客戶來電給公司同仁,不僅耗時而且容易失誤。本研究透過命名實體識別技術,自動擷取語音中的人名,透過雙數組Trie樹與AC自動機算法的技術,融合編輯距離的方法,進而找出公司同仁的人名。我們以精確率、召回率及F1 Score為評估方法,對不同類型語料的不同語音來源,進行語音人名辨識評估。最後我們設計了一個語音人名辨識系統,模擬一間小公司的電話轉接功能,以此驗證辨識性能。實驗結果顯示,可辨認出的在職員工的準確率為90.2%,而辨識出無此員工的準確率為88.32%,而整體的準確率達到89.73%。本研究成果可應用於公司總機的自動轉機。

關鍵詞:語音人名辨識;自動語音辨識、命名實體識別、雙數組Trie樹、AC自動機算法 
摘要(英) The company′s switchboard often needs to transfer customer calls to the company′s colleagues, which is not only time-consuming but also prone to errors. In this study, named entity recognition technology is used to automatically capture the names of people in speech, and the Double-Array Trie and Aho–Corasick algorithm are combined with the edit distance method to find out the names of colleagues in the company. We use precision, recall and F1 Score as evaluation methods to evaluate speech name recognition for different speech sources of different types of corpus. Finally, we designed a speech personal name recognition system to simulate the phone transfer function of a small company to verify the recognition performance. The experimental results show that the accuracy of identifying active employees is 90.2%, and the accuracy of identifying ex-employees or un-hired employees is 88.32%, and the overall accuracy is 89.73%. The research results can be applied to the automatic transfer of the company′s switchboard.

Keywords: speech personal name recognition; automatic speech recognition, named entity recognition, Double-Array Trie, Aho–Corasick algorithm.
關鍵字(中) ★ 語音人名辨識
★ 自動語音辨識
★ 命名實體識別
★ 雙數組Trie樹
★ AC自動機算法
關鍵字(英)
論文目次 摘要 i
Abstract ii
目錄 iii
圖目錄 v
表目錄 vii
第一章、 緒論 1
1.1 研究背景 1
1.2 研究目的 1
1.3 論文架構 3
第二章、 文獻回顧 4
2.1中文分詞與識別技術 4
2.1.1 隱藏式馬可夫模型(Hidden Markov Model,HMM) 4
2.1.2 條件隨機場(Conditional Random Fields,CRFs) 5
2.1.3 HMM與CRF之不同 6
2.1.4 詞性標註(Part-of-Speech tagging,POS tagging) 6
2.1.5 命名實體識別(Named Entity Recognition,NER) 8
2.2 NLP的深度模型技術 9
2.2.1 遞迴神經網路(Recurrent Neural Network,RNN) 10
2.2.2 長短期記憶(Long Short-Term Memory,LSTM) 11
2.2.3 門閘遞迴單元(Gated Recurrent Unit,GRU) 12
2.2.4 非等長結構(Sequence-to-sequence,seq2seq) 13
2.2.5 注意力機制(Attention Mechanism) 14
第三章、 語音人名辨識系統設計 17
3.1 MIAT方法論 17
3.1.1 IDEF0階層式模組化設計 17
3.1.2 GRAFCET離散事件建模 19
3.2 語音人名辨識系統架構 21
3.3 語音辨識 22
3.4 人名辨識 25
3.4.1 雙向GRU(Bidirectional Gate Recurrent Unit,Bi-GRU) 26
3.4.2 CRF分詞與序列標註 27
3.5 人名比對 29
3.5.1 雙數組Trie樹與AC自動機算法 30
3.5.2 萊文斯坦距離(Levenshtein Distance) 32
第四章、 實驗 37
4.1 語音辨識實驗 37
4.1.1 實驗評估方法 40
4.1.2 語音辨識實驗結果 41
4.2 人名辨識實驗 43
4.2.1 實驗評估方法 45
4.2.2 人名辨識實驗結果 45
4.3 注音轉換分析 46
4.3.1 實驗評估方法 48
4.3.2 注音轉換實驗結果 48
4.4 MIAT語音人名辨識 49
4.4.1 實驗評估方法 50
4.4.2 MIAT語音人名辨識結果 51
4.5 討論 52
4.5.1 分析無法辨識出人名 52
4.5.2 分析高於或低於標準分數 53
第五章、 結論與未來展望 57
5.1 結論 57
5.2 未來展望 58
參考文獻 59
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指導教授 陳慶瀚 審核日期 2022-7-4
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