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研究生:蘇鈺婷
論文名稱:以噪音分類為基礎之深度學習噪音消除法提升人工電子耳使用者之語音理解度表現
論文名稱(外文):A noise classification-based deep learning noise reduction approach to improve speech intelligibility for cochlear implant recipients
指導教授:葉榮木葉榮木引用關係賴穎暉
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:機電工程學系
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2017
畢業學年度:105
語文別:中文
論文頁數:74
中文關鍵詞:人工電子耳噪音消除DDAE噪音分類器深度學習
外文關鍵詞:cochlear implantnoise reductiondeep denoising autoencodernoise classificationdeep learning
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人工電子耳(cochlear implant, CI)是現今唯一可幫助全聾患者重新聽見聲音的重要科技。於過去的研究證明人工電子耳於安靜的溝通環境下能有效的幫助患者提升語音理解能力。但在噪音環境下,其效益仍存在許多改進空間,並期望能發展出更有效的訊號處理來提升使用者之滿意度。近年,一個基於深度學習理論所發展出的噪音消除方法被提出,即是 deep denoising autoencoder(DDAE)。其研究成果證明,DDAE 噪音消除法在人工電子耳模擬測試下,有顯著的語音理解力的改善效益。但對於真實人工電子耳使用者來說,其 DDAE 之效益仍未有研究證據。有鑑於此,本論文將基於 DDAE 噪 音 消 除 法 進 行 改 良 , 並 提 出 一 個 新 的 噪 音 消 除 方 法 , 稱 noise classification+DDAE (NC+DDAE)。此外,也將所提出之方法進行真實人工電子耳使用者之臨床效益驗證。從客觀之聲電指標驗證及語音聽辨力測試結果發現,在噪音環境下,NC+DDAE 能比兩個常見的傳統噪音消除法(logMMSE, KLT)有更佳之語音理解力表現,特別是噪音是己知情況。更具體的來說,當噪音情境是已知時,其 NC+DDAE 分別在不同測試條件下能比其他方法最多提升了 41.5 %之語音理解度表現;當噪音情境是未知的情況下其 NC+DDAE 能比其他方法最多提升了 17.5 %之語音理解度表現。有鑑於上述之結果證明,本論文所提出之 NC+DDAE 噪音消除法將能有效的提升人工電子耳使用者於噪音情境下之聆聽效益。
Cochlear implant (CI) is the only technology to help deaf hearing loss individual to hear sound again. Previous studies demonstrate that the CI technologies has enabled many CI users to enjoy a high level of speech understanding in quiet; however, for the most CI users, listening under noisy conditions remains challenging and desire the efficient signal processing be proposed to overcome this issue. More recently, deep learning-based NR approach, called deep denoising autoencoder (DDAE), have been proposed and confirmed to be effective in various NR tasks. In addition, the previous study indicated that the DDAE-based NR approach yielded higher intelligibility scores than those obtained with conventional NR techniques in CI simulation; however, the efficacy of the DDAE NR approach for real CI recipients remains unevaluated. In view of this, this study further to evaluate the performance of DDAE-based NR in real CI subject. In addition, a new DDAE-based NR model, called NC+DDAE, has been proposed in this study to further improve the intelligibility performance for CI users. The experimental results of objective evaluation and listening test indicate that, under challenging listening conditions, the proposed NC+DDAE NR approach yields higher intelligibility scores than two classical NR techniques (i.e., logMMSE, KLT), especially under match training condition. More specifically, the NC+DDAE improve speech recognition up to 41.5 % and 17.5 % at most under test conditions when the noise has ever been provided and never provided in training phase. The present study demonstrates that, under challenging listening conditions, the proposed NC+DDAE NR approach could improve speech recognition more effectively when compared to conventional NR techniques. Furthermore, the results shows that NC+DDAE has superior noise suppression capabilities, and provides less distortion of speech envelope information for Mandarin CI recipients, compared to conventional techniques. Therefore, the proposed NC+DDAE NR approach can potentially be integrated into existing CI processors to overcome the degradation of speech perception caused by noise.
摘要 I
Abstract II
誌謝 IV
目錄 V
圖目錄 VIII
表目錄 X
第一章 緒論 1
1.1 研究動機 1
1.2 研究目的 3
1.3 論文架構 4
第二章 文獻回顧 5
2.1 聽覺系統 5
2.2 聽力損失的原因與分類 6
2.3 人工電子耳設備介紹 7
2.3.1 人工電子耳使用者聽覺系統 8
2.3.2 人工電子耳之語音處理器 9
2.4 機械學習 11
2.4.1 人工神經網路 13
2.4.2 深層類神經網路 17
2.4.3 深度類神經網路之聲音情境分類 18
2.5 單麥克風之噪音消除策略 23
2.5.1 非監督式 24
2.5.2 監督式噪音消除法: DDAE 29
2.6 主、客觀效益評估方法 36
2.6.1 分類器準確率 36
2.6.2 客觀的聲電分析:NCM 36
2.6.3 語音辨識力測試 37
第三章 研究方法 39
3.1 提出方法:以噪音分類為基礎之深度學習噪音消除法 39
3.2 實驗設計一:客觀聲電效益驗證 41
3.2.1 噪音分類器 41
3.2.2 深度學習架構之噪音消除法 45
3.3 實驗設計二:臨床效益驗證 48
3.3.1 臨床實驗方法 48
3.3.2 評分方法 49
3.3.3 臨床實驗流程 49
3.4 受測者 50
3.5 實驗語料 53
3.5.1 訓練語料 54
3.5.2 測試語料 55
3.5.3 練習語料 56
第四章 實驗結果與討論 57
4.1 分類器之辨識效益 57
4.2 語音頻譜與胞絡線分析 60
4.3 客觀聲電效益驗證:NCM 63
4.4 臨床測試實驗結果 65
第五章 結論與未來展望 68
5.1 結論 68
5.2 未來展望 68
參考文獻 70
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