语音信号中的情感特征分析和识别的研究

本文分析了含有欢快、愤怒、惊奇、悲伤等４种情感语音信号的时间构造、振幅构造、基频构造和共振峰构造的特征。通过和不带情感的平静语音信号的比较,总结了不同情感语音信号的情感特征的分布规律。根据这些分析,提取了９个情感特征进行了情感识别的实验,获得了基本上接近于人的正常表现的识别结果。     

语音信号中的情感特征分析和识别的研究

提出了一种利用全局和时序结构的组合特征以及MMD进行情感特征识别的方法.对于从10名话者中搜集的带有欢快,愤怒,惊奇和悲伤4种情感的1000句语句,利用提出的新的识别方法获得了94%的平均情感识别率.     

语音情感识别研究综述

语音不仅包含所需要的文本内容,也包含所要表达的情感信息。情感识别是人机情感交互的关键。该研究从语音情感语料库、情感特征提取和情感识别的应用等方面对情感语音识别进行了综述,目的是让机器感知人类的情感状态,提高机器的人性化水平。     

语音情感识别综述

语音情感识别是利用计算机建立语音信息载体与情感度量之间的关系,并赋予计算机识别、理解人类情感的能力,语音情感识别在人机交互中起着重要作用,是人工智能领域重要发展方向。本文从语音情感识别在国内外发展历史以及开展的一系列会议、期刊和竞赛入手,分别从6个方面对语音情感识别的研究现状进行了梳理与归纳：首先,针对情感表达从离散、维度模型进行了阐述;其次,针对现有的情感数据库进行了统计与总结;然后,回顾了近20年部分代表性语音情感识别发展历程,并分别阐述了基于人工设计的语音情感特征的情感识别技术和基于端到端的语音情感识别技术;在此基础之上,总结了近几年的语音情感识别性能,尤其是近两年在语音领域的重要会议和期刊上的语音情感识别相关工作;介绍了语音情感识别在驾驶、智能交互领域、医疗健康,安全等领域的应用;最后,总结与阐述了语音情感识别领域仍面临的挑战与未来发展方向。本文旨在对语音情感识别相关工作进行深入分析与总结,为语音情感识别相关研究者提供有价值的参考。     

语音情感转换技术综述

随着情感信息处理的研究不断深入,语音信号中的情感转换越来越受到人们的重视。与传统的信息处理技术不同,语音的情感转换是用机器来实现理解和认识。本文首先探讨了情感的分类;接着,将语音情感转换系统分为：特征提取、参数转换和语音合成,并从特征提取和参数转换两方面进行了阐述,分析了相关的理论及算法,对各方法的优缺点进行了比较。最后,对语音情感转换研究方向进行了讨论。     

语音信号中情感特征的分析和识别

随着信息技术的发展，对人机交互能力的要求不断提高，情感信息处理已成为提高人机交互能力的一个重要课题。文中介绍了近年来国内外在语音信号中的情感特征分析和识别的研究概况，讨论了存在的一些问题和今后需要进一步研究的课题。     

实用语音情感的特征分析与识别的研究

 该文针对语音情感识别在实际中的应用,研究了烦躁等实用语音情感的分析与识别。通过计算机游戏诱发的方式采集了高自然度的语音情感数据,提取了74种情感特征,分析了韵律特征、音质特征与情感维度之间的关系,对烦躁等实用语音情感的声学特征进行了评价与选择,提出了针对实际应用环境的可拒判的实用语音情感识别方法。实验结果表明,文中采用的语音情感特征,能较好识别烦躁等实用语音情感,平均识别率达到75%以上。可拒判的实用语音情感识别方法,对模糊的和未知的情感类别的分类进行了合理的决策,在语音情感的实际应用中具有重要的意义。     

语音情感识别的研究进展

情感在人类的感知、决策等过程扮演着重要角色.长期以来情感智能研究只存在于心理学和认知科学领域,近年来随着人工智能的发展,情感智能跟计算机技术结合产生了情感计算这一研究课题,这将大大的促进计算机技术的发展.情感自动识别是通向情感计算的第一步.语音作为人类最重要的交流媒介,携带着丰富的情感信息.如何从语音中自动识别说话者的情感状态近年来受到各领域研究者的广泛关注.本文从语音情感识别所涉及的几个重要问题出发,包括情感理论及情感分类、情感语音数据库、语音中的情感特征和语音情感识别算法等,介绍了当前的研究进展,并讨论了今后研究的几个关键问题.     

基于SVM的语音情感识别研究

随着计算机技术的发展,人们对和谐人机交互的要求不断提高,这就要求计算机能理解说话人的情感信息,即能进行语音情感识别。本文提出了一种基于支持向量机（SVM）的语音情感识别方法,主要对人类的6种基本情感：高兴、惊奇、愤怒、悲伤、恐惧、平静进行研究。首先对自建语音情感数据库的情感语句提取特征,然后运用序列前向选择（SFS）算...     

语音情感识别的发展与展望

对语音情感识别的起源及主要研究内容作了介绍,对国内外语音情感识别的研究现状作了归纳总结;对语音情感特征的提取、情感分类器的建模算法作了重点分析介绍,最后对情感识别未来发展方向进行了展望.     

Noisy speech emotion recognition using sample reconstruction and multiple-kernel learning

Speech emotion recognition (SER) in noisy environment is a vital issue in artificial intelligence (AI). In this paper, the reconstruction of speech samples removes the added noise. Acoustic features extracted from the reconstructed samples are selected to build an optimal feature subset with better emotional recognizability. A multiple-kernel (MK) support vector machine (SVM) classifier solved by semi-definite programming (SDP) is adopted in SER procedure. The proposed method in this paper is demonstrated on Berlin Database of Emotional Speech. Recognition accuracies of the original, noisy, and reconstructed samples classified by both single-kernel (SK) and MK classifiers are compared and analyzed. The experimental results show that the proposed method is effective and robust when noise exists.     

集成语音信号与表面肌电信号的情感自动识别模型

为了提高情感识别的正确率,针对单一语音信号特征和表面肌电信号特征存在的局限性,提出了一种集成语音信号特征和表面肌电信号特征的情感自动识别模型.首先对语音信号和表面肌电信号进行预处理,并分别提取相关的语音信号和表面肌电信号特征,然后采用支持向量机对语音信号和表面肌电信号特征进行学习,分别建立相应的情感分类器,得到相应的识别结果,最后将识别结果分别输入到支持向量机确定两种特征的权重系数,从而得到最终的情感识别结果.两个标准语情感数据库的仿真结果表明,相对于其它情感识别模型,本文模型大幅提高了情感识别的正确率,人机交互情感识别系统提供了一种新的研究工具.     

人机交互中的语音情感识别研究进展

语音情感识别是当前信号处理、模式识别、人工智能、人机交互等领域的热点研究课题,其研究的最终目的是赋予计算机情感能力,使得人机交互做到真正的和谐和自然。本文综述了语音情感识别所涉及到的几个关键问题,包括情感表示理论、情感语音数据库、情感声学特征分析以及情感识别方法四个方面的最新进展,并指出了研究中存在的问题及下一步发展的方向。     

Multiple feature fusion for unimodal emotion recognition

A new semi-serial fusion method of multiple feature based on learning using privileged information(LUPI) model was put forward.The exploitation of LUPI paradigm permits the improvement of the learning accuracy and its stability,by additional information and computations using optimization methods.The execution time is also reduced,by sparsity and dimension of testing feature.The essence of improvements obtained using multiple features types for the emotion recognition(speech expression recognition),is particularly applicable when there is only one modality but still need to improve the recognition.The results show that the LUPI in unimodal case is effective when the size of the feature is considerable.In comparison to other methods using one type of features or combining them in a concatenated way,this new method outperforms others in recognition accuracy,execution reduction,and stability.     

Human emotion recognition by optimally fusing facial expression and speech feature

Emotion recognition is a hot research in modern intelligent systems. The technique is pervasively used in autonomous vehicles, remote medical service, and human–computer interaction (HCI). Traditional speech emotion recognition algorithms cannot be effectively generalized since both training and testing data are from the same domain, which have the same data distribution. In practice, however, speech data is acquired from different devices and recording environments. Thus, the data may differ significantly in terms of language, emotional types and tags. To solve such problem, in this work, we propose a bimodal fusion algorithm to realize speech emotion recognition, where both facial expression and speech information are optimally fused. We first combine the CNN and RNN to achieve facial emotion recognition. Subsequently, we leverage the MFCC to convert speech signal to images. Therefore, we can leverage the LSTM and CNN to recognize speech emotion. Finally, we utilize the weighted decision fusion method to fuse facial expression and speech signal to achieve speech emotion recognition. Comprehensive experimental results have demonstrated that, compared with the uni-modal emotion recognition, bimodal features-based emotion recognition achieves a better performance.     

Discriminative tonal feature extraction method in mandarin speech recognition

To utilize the supra-segmental nature of Mandarin tones, this article proposes a feature extraction method for hidden markov model (HMM) based tone modeling. The method uses linear transforms to project F0 (fundamental frequency) features of neighboring syllables as compensations, and adds them to the original F0 features of the current syllable. The transforms are discriminatively trained by using an objective function termed as "minimum tone error", which is a smooth approximation of tone recognition accuracy. Experiments show that the new tonal features achieve 3.82% tone recognition rate improvement, compared with the baseline, using maximum likelihood trained HMM on the normal F0 features. Further experiments show that discriminative HMM training on the new features is 8.78% better than the baseline.