韩纪庆
个性化签名
- 姓名:韩纪庆
- 目前身份:
- 担任导师情况:
- 学位:
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学术头衔:
博士生导师
- 职称:-
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学科领域:
耳鼻咽喉科学
- 研究兴趣:
韩纪庆,1964年生。1987年哈工大信息处理与模式识别专业毕业,获学士学位,校优秀毕业生。1990年3月哈工大模式识别与智能控制专业研究生毕业,获硕士学位,校优秀毕业研究生金牌奖(5%)。1994年考取哈工大计算机应用专业计划内在职博士生。1996年6月至1998年1月在韩国科学院系统工程研究所从事电话语音识别的合作研究工作,1998年6月获得博士学位。2001年被破格晋升为教授,2002年被破格评定为博士生导师,现为哈工大计算机学院院长助理、计算机科学与工程系副主任。中国人工智能学会机器感知与虚拟现实专业委员会委员、中国声学学会语言、听觉与音乐声学专业委员会委员、IEEE会员,中国计算机学会高级会员、中国声学学会高级会员,第六届、第七届、第八届全国人机语音通讯会议程序委员、第七届国际青年计算机会议程序委员。承担和完成过国家自然科学基金项目、国家863项目、教育部“跨世纪优秀人才培养计划”基金项目等。有多项成果获省部级科技进步奖。已在国内外刊物和会议上发表论文60余篇,论文多次被四检收录。
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韩纪庆, 张磊, , 王承发
,-0001,():
-1年11月30日
本文提出一种新的描述正常语音和变异语音之间关系的补偿因子。该补偿因子兼顾考虑了由于变异引起的特征分布中均值和方差的变化,并在k-均值初始化的参数基础上,采用EM 算法迭代估计变异补偿因子的值。通过估计出的补偿因子对变异语音特征进行补偿。对航空模拟飞行器中采集的应力变异下特定话者小词表孤立词的实验结果表明,利用所提出的方法可以将识别率提高32.3%。
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159浏览
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韩纪庆, 冯涛, 王承发
,-0001,():
-1年11月30日
本文从三个不同的角度分析了稳健水印应满足的性质,提出了相应的三个原则。对于不满足原则的水印给出了相应的转化方法,从而使处理不同类型的水印有了一个统一方法,并进行了相关实验。结果表明,在遵循上述原则的情况下,可以极大地提高水印的抗攻击能力。
数字水印, 水印结构, 熵率
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75浏览
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329下载
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韩纪庆, 张磊, 王承发
,-0001,():
-1年11月30日
文讨论了变异语音处理技术及其研究进展,分析了变异情况对语音识别性能产生的影响,综述了变异语音分类和变异语音识别方法,探讨了变异语音处理研究中存在的问题及未来的研究重点。
变异语音, 语音分析, 语音分类, 顽健语音识别
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89浏览
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399下载
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韩纪庆, 邵艳秋
,-0001,():
-1年11月30日
本文介绍了基于语音信号的情感处理技术的研究进展,综述了该领域的研究方法、相关技术及应用领域,讨论了其未来的发展方向。
语音信号, 情感, 语音识别, 语音合成
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117浏览
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605下载
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韩纪庆, 王欢良, 李海峰, 郑铁然
,-0001,():
-1年11月30日
在语言发音学习中,有效的反馈对学习者有很大的帮助。计算机辅助发音学习系统可以给学习者有效的发音指导。本文就目前基于语音识别的发音学习技术进行介绍,给出系统原理框图,对一些关键技术和问题进行探讨,最后对其发展进行展望。
发音学习, 语音识别, 自动发音打分, 计算机辅助语言学习
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90浏览
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545下载
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【期刊论文】Classification of Speech Under G-Force Based on TEO Pitch*
韩纪庆, JiQing Han, YongLin Ma, Lei Zhang, ChengFa Wang
,-0001,():
-1年11月30日
Speech production variations due to perceptually induced stress contribute significantly to reduced speech processing performance. One approach that can improve the robustness of speech recognition against stress is to formulate an objective classification of speaker stress based on the acoustic speech signal. Special processing could then be applied once non-neutral stress states are detected. Thus, it is very important to study an effective classification method of speech under stress. So far, there are little works about the conditions of G-Force in the studies of stressed speech. In this paper, we investigated the speech features of pitch and TEO pitch for classification of neutral speech and speech under G-Force. Both Bayesian hypothesis and HMM classifier are employed for stress classification. Experimental results show that TEO pitch is better than pitch for classification of speech under G-Force and HMM classifier is also better than Bayesian classifier. For HMM classifier, using both TEO pitch and its delta feature is better than just using TEO pitch, 89.2% and 97.3% classification rates are gotten for two speakers, respectively.
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67浏览
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102下载
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【期刊论文】An Environment Adaptation Method For Robust Speech Recognition
韩纪庆, HAN JiQing, ZHANG Lei, WANG Chengfa
,-0001,():
-1年11月30日
It is well known that differences between training and testing environments seriously affect speech recognition accuracy. Several environment adaptation techniques have been proposed for eliminating environmental difference, and they are effective when the adaptation data in the testing environment is available beforehand. However, testing environments are not always known beforehand. Therefore, a new framework, which uses testing utterances themselves for adaptation, is effective to cope with such variation. In this paper, a method of testing environment adaptation is proposed, which radually learns the features of testing environment as the testing procedure, and does not need to get some testing samples beforehand.
Robust speech recognition, Environment adaptation, Environment model
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53浏览
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【期刊论文】ROBUST SPEECH RECOGNITION METHOD BASED ON DISCRIMINATIVE LEARNING OF ENVIRONMENTAL FEATURES
韩纪庆, Jiqing Han*, Munsung Han**, Gyu-Bong Park**, Jeongue Park**, Chengfa Wang*
,-0001,():
-1年11月30日
Learning the influence of additive noise and channel distortions from training data is an effective approach for robust speech recognition. We had proposed a novel method of discriminative learning environmental features according to Minimum Classification Error (MCE) criterion in the previous work, in which additive noise are expressed by the weighted combination of multiple types of noises, and the channel distortions are assumed to be consisted of the channel distortions of the whole training data and the current utterance. In this paper, we use a Gaussian distribution to stand for the distribution of additive noise, and adaptive learn the combination factors of the channel distortions. The current method has been proved better than the former one by experiments.
Robust speech recognition, Additive noise, Channel distortion
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57浏览
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韩纪庆, Jiqing Han*, **, Munsung Han*, Gyu-Bong Park*, Jeongue Park*, Wen Gao**
,-0001,():
-1年11月30日
Learning the influence of additive noise and channel distortions from training data is an effective approach for robust speech recognition. Most of the previous methods are based on maximum likelihood estimation criterion. In this paper, we propose a new method of discriminative learning environmental parameters, which is based on Minimum Classification Error (MCE) criterion. By using a simple classifier defined by ourselves and the Generalized Probabilistic Descent (GPD) algorithm, we iteratively learn environmental parameters. After getting the parameters, we estimate the clean speech features from the observed speech features and then use the estimation of the clean speech features to train or test the back-end HMM classifier. The best error rate reduction of 32.1% is obtained, tested on a Korean 18 isolated confusion words task, relative to conventional HMM system.
Robust speech recognition, Additive noise, Channel distortion
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94浏览
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【期刊论文】Robust telephone speech recognition based on channel compensation
韩纪庆, Jiqing Han*, Wen Gao
Pattern Recognition 32(1999)1061-1067,-0001,():
-1年11月30日
Channel compensation technique has been proved to be an effective approach for robust speech recognition. In this paper, we compare the performance of our proposed method RMFCC with those of the former channel compensation methods: CMS, two-level CMS and RASTA for robust telephone speech recognition. For all experiments, a Korean isolated 84-word-database consisting of 80 speakers collected from local telephone line is adopted. Using RMFCC, a 39.8% reduction in word error rate is obtained relative to conventionalHMMsystem. It is shown from the experiments that RMFCC, comparing with RASTA, reduces the computational complexity without losing accuracy, and is also better than CMS and two-level CMS on the performance. After discussion, we verify that it is an effective approach to suppress very low modulation frequencies by fjiltering for robust telephone speech recognition.
Channel compensation, Speech recognition, Robustness, Modulation frequencies, Signal-to-noise rate
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64浏览
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