Description: 里面包含了比较新的国外说话人识别方面的新文章,观点比较新,可以翻译过来使用,在其中加入一点新算法或者混用就可以成新的观点了,全英文,不带翻译-Which contains a relatively new speaker recognition abroad, the new article, a relatively new perspective can be translated through the use, in which the new algorithm by adding one point or you can mix into a new perspective, and the whole English, without translation Platform: |
Size: 2194432 |
Author:tanruilian |
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Description: 语音识别的电子书籍，希望和相关专业的朋友能够分享-Speech Recognition e-books, hope and the relevant professional friends to share Platform: |
Size: 1107968 |
Author:yangshuai |
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Description: Generalized Mel frequency cepstral coefficients for large-vocabulary Speaker-Independent Continuous-Speech Recognition 关于MFCC算法的很好的英语文章-Generalized Mel frequency cepstral coefficients for large-vocabulary Speaker-Independent Continuous-Speech Recognition on the MFCC algorithm is a very good article in English Platform: |
Size: 165888 |
Author:xiang |
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Description: 语音识别，一个完整的C语言程序，基于DSP5402开发板，能有效识别男女，及说话人，程序仅供参考-Speech recognition, a complete C language program, based on the development board DSP5402 can effectively identify the men and women, and the speaker, the program is for reference only Platform: |
Size: 62464 |
Author:马九洋 |
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Description: 摘要：提出了一种新的说话人识别中特征参数的提取方法*在分别使用傅立叶分析和小波分析得到两组特征
参数之后，进一步利用+,-./0准则进行参数选取，构造了一种新的混合特征参数*在不增加训练和识别时计算
量的同时，结合了傅立叶分析和小波分析两者的优点，具有更好的分类能力*实验结果显示，这种新的混合参数
有效地提高了说话人的识别率，能更好地表征说话人的特征*-Abstract: This paper presents a new kind of speaker recognition in the characteristic parameter extraction method*, respectively, using Fourier analysis and wavelet analysis of two sets of characteristic parameters, the further use of 2B !,-./ 0 criteria for parameter selection, construction A new mixed feature parameters* at no additional training and recognition computation At the same time, a combination of Fourier analysis and wavelet analysis of the advantages of both and has better classification capability* Experimental results show that the new mixing parameters effectively enhance the speaker Platform: |
Size: 139264 |
Author:王功臣 |
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Description: 摘要：一般的说话人识别系统包括特征提取和识别模型两部分，其中特征参数的选择对系统
的识别性能有关键性的影响，现就特征提取展开研究，介绍了各种常用的语音特征参数及目前
主流的两种参数的提取过程，并论述了小波分析应用于语音特征参数提取中的优势-Abstract: The general speaker recognition system includes feature extraction and recognition model in two parts, of which the choice of the characteristic parameters of the recognition performance of the system have a crucial impact on the feature extraction is to commence the study, introduce a variety of commonly used voice feature parameters and At present, the mainstream of the two parameters of the extraction process, and discusses the wavelet analysis applied to voice characteristic parameter extraction of the advantages of Platform: |
Size: 63488 |
Author:王功臣 |
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Description: 常用的说话人识别方法有模板匹配法、统计建模法、联接主义法(即人工神经网络实现)。考虑到数据量、实时性以及识别率的问题，采用基于矢量量化和隐马尔可夫模型(HMM)相结合的方法。
说话人识别的系统主要由语音特征矢量提取单元(前端处理)、训练单元、识别单元和后处理单元组成，
-Commonly used methods of speaker recognition template matching method, statistical modeling method, and connection method (ie, artificial neural networks). Taking into account the amount of data, real-time as well as the recognition rate of the problem, based on vector quantization and Hidden Markov Model (HMM) method of combining. Speaker recognition system mainly by the voice feature vector extraction unit (front-end treatment), training modules, identification and post-treatment unit modules, Platform: |
Size: 64512 |
Author:孙丽 |
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Description: vector quantization with the weight for
the speaker recognization-vector quantization with the weight forthe speaker recognization Platform: |
Size: 2048 |
Author:陈哲 |
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Description: The speech signal contains rich messages, and three main recognition fields from speech signal, which are of most interest and have been studied for several decades, are speech recognition, language recognition and speaker recognition. In this the focus is on speech recognition field. In our everyday lives there are many forms of communication, for instance: body language, textual language, pictorial language and speech, etc. However amongst those forms speech is always regarded as the most powerful form because of its rich dimensions character. Except for the speech text (words), the rich dimensions also refer as the gender, attitude, emotion, health situation and identity of a speaker. Such information is very important for an effective communication. From the signal processing point of view, speech can be characterized in terms of the signal carrying message information. The waveform could be one of the representations of speech, and this kind of signal has been most useful in practical applications. Platform: |
Size: 3072 |
Author:kinny garg |
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Description: speech recognition using hidden markov model making it speaker independent
Using Matlab 7.5[2008b] Platform: |
Size: 10031104 |
Author:kani |
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Description: 说话人识别中,最后带测语音匹配阶段.利用DTW算法实现-Speaker recognition, the last swath of voice matching stage. The use of DTW algorithm Platform: |
Size: 1024 |
Author:雷硕 |
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Description: 这个是用Matlab写成的说话人识别演示程序。-Currently, there are more than 20 classes in this library, including commonly used feature extraction algorithms and modeling techniques for speech recognition and speaker verification.-Currently, there are more than 20 classes in this library. including commonly used feature extraction al gorithms and modeling techniques for re speech cognition and speaker verification. Platform: |
Size: 2451456 |
Author:liximin |
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Description: this code contain function and simple program for voice pattern recognition using MELCEPST Platform: |
Size: 4096 |
Author:kotakungu |
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Description: 语音识别，可以将说话者的声音识别出来并在相应位置显示-Speech recognition, voice of the speaker can be identified and displayed in the corresponding position Platform: |
Size: 4086784 |
Author:冯文娟 |
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Description: 语音信号数字处理是一门涉及面很广的交叉科学，这本书介绍了语音信号的数字表示，矢量量化，隐马尔科夫模型，语音合成，语音识别，语音增强，说话人识别等知识-Speech signal digital processing is a wide-ranging cross-science, this book describes a digital representation of the speech signal, vector quantization, hidden Markov models, speech synthesis, speech recognition, speech enhancement, speaker recognition of such knowledge Platform: |
Size: 23527424 |
Author:张玉 |
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