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71.
在语音情感识别过程中,为解决缺乏方言数据库、识别模型准确率低等问题,建立辽西方言语音情感数据库,并提出一种融合注意力机制轻量级网络的语音情感识别模型。模型由特征组合网络、CBAM注意力机制、深度卷积网络及输出层四部分组成。利用三个大小不同的并行卷积提取浅层语音特征并进行拼接;引入CBAM注意力模块将空间特征与通道特征融合;融合后的特征输入深度卷积网络,提取语音深层次特征,输出多维特征向量;输出层对语音进行情感分类识别。模型在IEMOCAP、Emo-DB和自建辽西语音情感数据库上验证,分别取得82.5%、96.2%和90.8%的准确率。实验结果表明,与其他深度学习的模型相比,本文提出的模型在参数量更少的同时识别率更高。 相似文献
72.
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。 相似文献
73.
针对英文情感分类问题,对不同样本采用不同权重,通过引入模糊隶属度函数,通过计算样本模糊隶属度确定样本隶属某一类程度的模糊支持向量机分类算法,通过对比选取不同核函数和不同惩罚系数的结果.仿真实验结果表明应用模糊支持向量机进行英文情感分类具有较好的分类能力和较高的识别能力. 相似文献
74.
Xin Zuo Chi Zhang Timo Hmlinen Hanbing Gao Yu Fu Fengyu Cong 《Entropy (Basel, Switzerland)》2022,24(9)
Emotion recognition based on electroencephalography (EEG) has attracted high interest in fields such as health care, user experience evaluation, and human–computer interaction (HCI), as it plays an important role in human daily life. Although various approaches have been proposed to detect emotion states in previous studies, there is still a need to further study the dynamic changes of EEG in different emotions to detect emotion states accurately. Entropy-based features have been proved to be effective in mining the complexity information in EEG in many areas. However, different entropy features vary in revealing the implicit information of EEG. To improve system reliability, in this paper, we propose a framework for EEG-based cross-subject emotion recognition using fused entropy features and a Bidirectional Long Short-term Memory (BiLSTM) network. Features including approximate entropy (AE), fuzzy entropy (FE), Rényi entropy (RE), differential entropy (DE), and multi-scale entropy (MSE) are first calculated to study dynamic emotional information. Then, we train a BiLSTM classifier with the inputs of entropy features to identify different emotions. Our results show that MSE of EEG is more efficient than other single-entropy features in recognizing emotions. The performance of BiLSTM is further improved with an accuracy of 70.05% using fused entropy features compared with that of single-type feature. 相似文献
75.
Recently, emotional electroencephalography (EEG) has been of great importance in brain–computer interfaces, and it is more urgent to realize automatic emotion recognition. The EEG signal has the disadvantages of being non-smooth, non-linear, stochastic, and susceptible to background noise. Additionally, EEG signal processing network models have the disadvantages of a large number of parameters and long training time. To address the above issues, a novel model is presented in this paper. Initially, a deep sparse autoencoder network (DSAE) was used to remove redundant information from the EEG signal and reconstruct its underlying features. Further, combining a convolutional neural network (CNN) with long short-term memory (LSTM) can extract relevant features from task-related features, mine the correlation between the 32 channels of the EEG signal, and integrate contextual information from these frames. The proposed DSAE + CNN + LSTM (DCRNN) model was experimented with on the public dataset DEAP. The classification accuracies of valence and arousal reached 76.70% and 81.43%, respectively. Meanwhile, we conducted experiments with other comparative methods to further demonstrate the effectiveness of the DCRNN method. 相似文献
76.
Cross-corpus speech emotion recognition (SER) is a challenging task, and its difficulty lies in the mismatch between the feature distributions of the training (source domain) and testing (target domain) data, leading to the performance degradation when the model deals with new domain data. Previous works explore utilizing domain adaptation (DA) to eliminate the domain shift between the source and target domains and have achieved the promising performance in SER. However, these methods mainly treat cross-corpus tasks simply as the DA problem, directly aligning the distributions across domains in a common feature space. In this case, excessively narrowing the domain distance will impair the emotion discrimination of speech features since it is difficult to maintain the completeness of the emotion space only by an emotion classifier. To overcome this issue, we propose a progressively discriminative transfer network (PDTN) for cross-corpus SER in this paper, which can enhance the emotion discrimination ability of speech features while eliminating the mismatch between the source and target corpora. In detail, we design two special losses in the feature layers of PDTN, i.e., emotion discriminant loss and distribution alignment loss . By incorporating prior knowledge of speech emotion into feature learning (i.e., high and low valence speech emotion features have their respective cluster centers), we integrate a valence-aware center loss and an emotion-aware center loss as the to guarantee the discriminative learning of speech emotions except an emotion classifier. Furthermore, a multi-layer distribution alignment loss is adopted to more precisely eliminate the discrepancy of feature distributions between the source and target domains. Finally, through the optimization of PDTN by combining three losses, i.e., cross-entropy loss , , and , we can gradually eliminate the domain mismatch between the source and target corpora while maintaining the emotion discrimination of speech features. Extensive experimental results of six cross-corpus tasks on three datasets, i.e., Emo-DB, eNTERFACE, and CASIA, reveal that our proposed PDTN outperforms the state-of-the-art methods. 相似文献
77.
研究表明儿童时期具有的情绪能力,是他们以后生活中能否成功的最好预示.虽然情绪是一种主观意识,但情绪状态的变化总会伴随一定的生理变化.从而我们可以从生理参数的角度,用一套客观的评价标准来研究情绪状态.为了让测试儿童处在一个相对自然的状态,使情绪识别结果的可信度更高,我们提出了\"穿戴式\"生理参数采集系统的概念.本文的研究重... 相似文献
78.
现有的智能教室中多通道融合方法普遍缺乏情境信息的感知能力,融合策略固定、简单,不能很好解决多通道输入的二义性、非精确、冲突性和时间偏序关系。针对以上问题,采用EMMA标注语言调整时序关系,用层次任务网络规划器HTN规划动作行为,用证据理论融合各个情感检测通道的检测结果,提出了一种通用可扩展的基于情境感知的多通道融合模型及方法。实验结果表明,该方法较好地解决了多通道学生情感检测的冲突性、二义性,提高了检测的精确性与正确性。 相似文献
79.
Gareth Lewis 《Research in Mathematics Education》2013,15(1):70-86
This paper reports some initial findings from research designed to understand more deeply the motivational and emotional landscape of disaffection with school mathematics. A context is described in which there has been significant concern expressed about a number of aspects of mathematics education, but where affect is seen as salient to these problems, including levels of attainment. A case is made that a focus on the qualitative study of motivation and emotion may be more central to an understanding of the phenomenon of disaffection than that of a quantitative study of attitude. The study involved students at two Further Education Colleges who had performed poorly in national examinations, but were required to continue studying mathematics. It was expected that many of them would be disaffected with mathematics. A mixed method approach was adopted, in which students were asked to complete a simple questionnaire on their experience of emotion in mathematics classrooms, and were then interviewed using a range of procedures to elicit qualitative data about their experience of mathematics. Reversal Theory was used as a framework to inform the design of the methods used and analysis of the data. Results demonstrate the richness and volatility of their motivational and emotional experiences of mathematics. 相似文献
80.
《不成问题的问题》是老舍对抗战大局和抗战文艺综合而深入思考的一种集中体现。它既是老舍反思抗战文艺重宣传、轻艺术的一种转向,也是他深受抗战时局之刺激和抗战建国理想的感召,思考抗战文艺如何更好地反映现实,进而为新中国的未来建设服务。在这一过程中,老舍作为文化审视和人性批判者的意识被再度激发强化。他借树华农场这一经济实体在抗战时期如何运作的细致剖析,从经济问题延伸到了文化和人性问题,也牵涉出对抗战文艺如何处理“情”与“理”之关系的多重思考。老舍的“情”“理”之辩,融合了他对抗战社会内在运作机制的犀利观察,实践了他对抗战文艺如何切实突进现实与生活的新思考,也寄托了他对抗战文艺淬炼民族文化传统、参与现代民族国家建构的殷切期待。 相似文献