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研究生： 黃姿云
Huang, Tzu-Yun
論文名稱： 雙重互補聲學嵌入網絡: 從原始波形挖掘與特徵集相異的語音情感識別特徵
A Dual Complementary Acoustic Embedding Network: Mining Discriminative Characteristics from Raw-waveform for Speech Emotion Recognition
指導教授： 李祈均
Lee, Chi-Chun
口試委員: 曹昱
Tsao, Yu
李宏毅
Lee, Hung-Yi
陳冠宇
Chen, Kuan-Yu
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年： 2019
畢業學年度： 107
語文別： 英文
論文頁數： 43
中文關鍵詞： 語音情緒辨識 、原始波型 、端對端學習 、聲學空間擴大
外文關鍵詞： speech emotion recognition, raw waveform, end-to-end learning, acoustic space augmentation
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    • 語音情緒辨識近年來在眾多廣泛的領域中成為一股潮流，同時憑藉著深度學習這項技術下，達到了令人驚豔的的成果。然而，因為端對端學習使用了複雜且時變性的原始波形導致它很難超越被精細設計過的特徵集。特徵集的擴大透過互補特徵的啟發, 這個方法可以同時利用辨別能力區別兩邊。在本篇論文中，我們提出了雙重互補聲學嵌入網路(DCaEN)的方法，透過將專家裁切特徵和原始波形模組在一起進而改進情緒辨識能力。基於專家設計用來使用在情感計算的聲學特徵集，我們使得餘弦相似性優化到負值來當作互補的限制，藉此限制來挖掘原始波形中額外資訊。在我們的實驗中，我們將所提出的模型應用於IEMOCAP和MSP-IMPROV 資料庫上，其準確率分別達到59.31%和46.22%，這結果皆比單單使用原始波形或是特徵集來的高。再者，在被學習的互補空間中，我們透過視覺化分析來更進一步的突出我們所提出的互補限制所造成的效果。

    Speech emotion recognition has recently become a trend in broader fields and has achieved stunning performance with deep learning technology. However, end-to-end learning using complex and time-varying raw waveform can still hardly exceed the finely-designed hand-crafted feature sets. A feature space augmentation approach by complementary feature elicitation can simultaneously leverage the discriminative power of both sides. In this study we propose a Dual Complementary Acoustic Embedding Network (DCaEN) jointly modeling hand-crafted features with raw waveform to improve emotion recognition. We specify the cosine distance to negative value as a complementary constraint to mine additional information from raw waveform in terms of expert-designed acoustic feature set for affective computing.
    Experimental results of predicting emotion categories on IEMOCAP and MSP-IMPROV database show that our proposed model achieves 59.31% and 46.22% respectively which both outperform the networks using either raw waveform or feature set solely. Moreover, we present visualization analysis on the learned complementary space to further illuminate the effect of complementary constraint.

    誌謝 I 摘要 II ABSTRACT III CONTENTS IV CHAPTER 1 INTRODUCTION 1 CHAPTER 2 DATABASE AND FEATURES 5 2.1 DATABASE 5 2.1.1 IEMOCAP 5 2.1.2 MSP-IMPROV 6 2.2 FEATURES 8 2.2.1 eGeMAPS 8 2.2.2 emobase 2010 9 2.2.3 Preprocessing on raw waveform 9 CHAPTER 3 RESEARCH METHODOLOGY 10 3.1 NEURAL NETWORK 10 3.1.1 Deep Neural Network (DNN) 12 3.1.2 Convolution Neural Network (CNN) 12 3.1.3 Long-Short Term Memory (LSTM) 15 3.1.4 Attention Mechanism 18 3.2 DUAL COMPLEMENTARY ACOUSTIC EMBEDDING NETWORK (DCAEN) 20 3.2.1 Feature Network (Stage 1) 21 3.2.2 Raw Waveform Complementary Network (Stage 2) 21 CHAPTER 4 EXPERIMENTS AND RESULTS 24 4.1 EXPERIMENT SETTING 24 4.2 NETWORK CONFIGURATION 24 4.3 RECOGNITION RESULTS ON IEMOCAP 26 4.3.1 eGeMAPS 26 4.3.1.1 Comparison Models 26 4.3.1.2 Results 27 4.3.2 emobase 2010 29 4.4 RECOGNITION RESULTS ON MSP-IMROV 30 4.4.1 eGeMAPS 30 4.4.1 emobase 2010 31 4.5 COMPLEMENTARY LEVEL ANALYSIS 32 4.5.1 Analysis on IEMOCAP 33 4.5.2 Analysis on MSP-IMPROV 34 CHAPTER 5 CONCLUSION 37 REFERENCE 38

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