結合心理特徵與情緒標籤訓練之語音情感辨識技術

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姓名

陳靖明(Jing-Ming Chen) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

結合心理特徵與情緒標籤訓練之語音情感辨識技術
(Speech Emotion Recognition Based on Joint Training by Self-Assessment Manikins and Emotion Labels)

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摘要(中)

隨著人工智慧的發展，人與機器之間的互動變得越加頻繁，如聊天機器人或居家照護系統都是常見的人機互動應用。而情感辨識技術可以用來提升人機之間的互動性，亦可將情緒機器人應用於醫療方面，如病患的情緒識別等。我們希望利用深度學習的技術來學習語音訊號中的情緒特徵，達到情感辨識的效果。
本研究為「結合心理特徵與情緒標籤訓練之語音情感辨識技術」，提出藉由結合心理狀態程度的情緒特徵，輔助情緒標籤訓練神經網路，來提升語音情感的辨識率。本研究同時使用了迴歸模型以及分類模型，迴歸模型用來進行心理狀態程度的預測，而分類模型則是用來進行情緒標籤的辨識。此語音情感辨識技術於腳本與即興演出混合情境的資料集中，辨識率能夠達到64.70%，若於只有即興演出情境的資料集，辨識率則是能達到66.34%，相對於未結合心理狀態特徵的辨識技術，此方法的辨識率各自提升了2.95%以及2.09%，因此結合心理狀態的特徵能夠有效地幫助語音情感進行辨識。

摘要(英)

With the development of artificial intelligence, the interaction between humans and machines has become more and more often, such as chat robots or home care systems, which are common human-computer interaction applications. Emotional recognition can improve the interaction between man and machine, and can also apply the emotional recognition of the robot to medical aspects, such as emotional identification of patients. The objective of this work is to develop a speech emotion recognition system by learning the emotional characteristics of audio using deep learning.
In this work, we propose a system that can recognize speech emotion and use both regression models and classification models. This speech emotion recognition technology can achieve the accuracy of 64.70% in the dataset of script and improvised mixed scenes. If the dataset has only impromvised scenes, the accuracy can reach 66.34%. Compared with the characteristics of uncombined mental state, the accuracy of this technology is increased by 2.95% and 2.09%, respectively. So the characteristics of mental state can effectively help the speech emotion recognition.

關鍵字(中)

★ 語音情緒辨識
★ 心理狀態特徵
★ 深度學習
★ 卷積遞迴神經網路

關鍵字(英)

★ Speech emotion recognition
★ Self-Assessment Manikin
★ Deep learning
★ Convolutional recurrent neural network.

論文目次

摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vii
表目錄 x
第一章緒論 1
1-1 研究背景 1
1-2 研究動機與目的 1
1-3 論文架構 2
第二章語音情緒辨識 3
2-1 情緒相關介紹 3
2-1-1 PAD情緒狀態模型 5
2-1-2 自我評估人體模型 6
2-2 聲學特徵的提取技術 7
2-2-1 時頻譜 8
2-2-2 對數梅爾刻度時頻譜 11
2-2-3 梅爾倒頻譜係數 14
第三章類神經網路與深度學習 15
3-1 類神經網路 15
3-1-1 單層感知機 17
3-1-2 多層感知機與倒傳遞演算法 18
3-2 深度學習 22
3-2-1 卷積神經網路 22
3-2-2 遞迴神經網路 25
3-2-3 長短期記憶神經網路 27
3-2-4 門閘遞迴單元神經網路 29
3-2-5 雙向遞迴神經網路 31
第四章提出之架構 32
4-1 語音前處理 33
4-2 卷積遞迴神經網路架構 35
4-3 訓練階段參數設定 36
第五章實驗與分析 37
5-1 實驗環境與資料庫 37
5-2 評比標準 40
5-3 實驗結果比較與分析 41
第六章結論與未來展望 51
參考文獻 52

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指導教授

張寶基(Pao-Chi Chang)

審核日期

2019-7-29

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