摘要翻译：
本文研究了实用的语音情绪识别技术，包括与认知相关的几种情绪类型，即烦躁、自信和疲倦。自然主义情感语音数据的高质量是本研究的基础。诱发实际情感言语的技巧有：认知任务、电脑游戏、噪音刺激、睡眠剥夺和电影片段。基于高斯混合模型研究了一个实用的语音情感识别系统。在小样本情况下，采用两类分类器集进行性能改进。考虑连续情感语音中的上下文信息，提出了一种嵌入马尔可夫网络的混合高斯模型。对系统的鲁棒性进行了进一步的研究。首先，将基于听觉掩蔽特性的降噪算法引入到实际的语音情感识别中。其次，针对实际情况下复杂的未知情感类型，提出了一种具有拒绝能力的情感识别方法，增强了系统对未知情感样本的兼容性。第三，针对大量未知说话人带来的困难，提出了一种基于说话人敏感特征聚类的情感特征归一化方法。第四，通过加入心电图通道，提出了一种基于语音信号和心电图信号的双模态情感识别系统。本文所研究的语音情感识别方法可以扩展到跨语言语音情感识别和耳语语音情感识别。
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英文标题：
《Research on several key technologies in practical speech emotion
  recognition》
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作者：
Chengwei Huang
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最新提交年份：
2017
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Sound        声音
分类描述：Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
涵盖了声音计算的各个方面，以及声音作为一种信息通道。包括声音模型、分析和合成、音频用户界面、数据的可听化、计算机音乐和声音信号处理。包括ACM学科类H.5.5，并与H.1.2、H.5.1、H.5.2、I.2.7、I.5.4、I.6.3、J.5、K.4.2交叉。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Audio and Speech Processing        音频和语音处理
分类描述：Theory and methods for processing signals representing audio, speech, and language, and their applications. This includes analysis, synthesis, enhancement, transformation, classification and interpretation of such signals as well as the design, development, and evaluation of associated signal processing systems. Machine learning and pattern analysis applied to any of the above areas is also welcome.  Specific topics of interest include: auditory modeling and hearing aids; acoustic beamforming and source localization; classification of acoustic scenes; speaker separation; active noise control and echo cancellation; enhancement; de-reverberation; bioacoustics; music signals analysis, synthesis and modification; music information retrieval;  audio for multimedia and joint audio-video processing; spoken and written language modeling, segmentation, tagging, parsing, understanding, and translation; text mining; speech production, perception, and psychoacoustics; speech analysis, synthesis, and perceptual modeling and coding; robust speech recognition; speaker recognition and characterization; deep learning, online learning, and graphical models applied to speech, audio, and language signals; and implementation aspects ranging from system architecture to fast algorithms.
处理代表音频、语音和语言的信号的理论和方法及其应用。这包括分析、合成、增强、转换、分类和解释这些信号，以及相关信号处理系统的设计、开发和评估。机器学习和模式分析应用于上述任何领域也是受欢迎的。感兴趣的具体主题包括：听觉建模和助听器；声波束形成与声源定位；声场景分类；说话人分离；有源噪声控制和回声消除；增强；去混响；生物声学；音乐信号的分析、合成与修饰；音乐信息检索；多媒体音频和联合音视频处理；口语和书面语建模、切分、标注、句法分析、理解和翻译；文本挖掘；言语产生、感知和心理声学；语音分析、合成、感知建模和编码；鲁棒语音识别；说话人识别与特征描述；应用于语音、音频和语言信号的深度学习、在线学习和图形模型；以及从系统架构到快速算法的实现方面。
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英文摘要：
  In this dissertation the practical speech emotion recognition technology is studied, including several cognitive related emotion types, namely fidgetiness, confidence and tiredness. The high quality of naturalistic emotional speech data is the basis of this research. The following techniques are used for inducing practical emotional speech: cognitive task, computer game, noise stimulation, sleep deprivation and movie clips.   A practical speech emotion recognition system is studied based on Gaussian mixture model. A two-class classifier set is adopted for performance improvement under the small sample case. Considering the context information in continuous emotional speech, a Gaussian mixture model embedded with Markov networks is proposed.   A further study is carried out for system robustness analysis. First, noise reduction algorithm based on auditory masking properties is fist introduced to the practical speech emotion recognition. Second, to deal with the complicated unknown emotion types under real situation, an emotion recognition method with rejection ability is proposed, which enhanced the system compatibility against unknown emotion samples. Third, coping with the difficulties brought by a large number of unknown speakers, an emotional feature normalization method based on speaker-sensitive feature clustering is proposed. Fourth, by adding the electrocardiogram channel, a bi-modal emotion recognition system based on speech signals and electrocardiogram signals is first introduced.   The speech emotion recognition methods studied in this dissertation may be extended into the cross-language speech emotion recognition and the whispered speech emotion recognition.
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PDF链接：
https://arxiv.org/pdf/1709.09364