Description: 在MATLAB环境下实现基于矢量量化的说话人识别系统。在实时录音的情况下，利用该说话人识别系统，对不同的人的1s～7s的语音进行辨识。实现与文本无关的自动说话人确认的实时识别。
使用说明：
1 训练
打开Matlab 使Current Directory为VQ所在的文件夹(比如：E:\vq)
在Command windows中输入
-in MATLAB environment based VQ Speaker Recognition System. Real-time recording in the circumstances, the speaker recognition system, the different people of one's voice-7s for identification. Implementation and text-independent speaker recognition automatic real-time identification. Use : Matlab opened a training Current Directory to make VQ where the folder (for example, : E : \ vq) in the Command windows input Platform: |
Size: 706560 |
Author:annya |
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Description: 说话人识别是语音识别的一种特殊方式，其目的不是识别语音内容，而是识别说话人是谁，即从语音信号中提取个人特征。采用矢量量化（VQ）可避免困难的语音分段问题和时间归整问题，且作为一种数据压缩手段可大大减少系统所需的数据存储量。本文提出了识别特征选取采用复倒谱特征参数和对应用VQ的说话人识别系统改进的一种方法。当用于训练的数据量较小时，复倒谱特征可以得到比较稳定的识别性能。VQ的改进方法避免了说话人识别系统的训练时间与使用时间相差过长从而导致系统的性能明显下降以及若利用自相关函数带来的大量运算。-Speaker Recognition Speech Recognition is a special way, its purpose is not voice recognition, Who identification but said that the voice signal from extracting personal characteristics. Vector quantization (VQ) can avoid the difficulties subparagraph voice to the issues and the whole time, and as a means of data compression system can significantly reduce the required data storage capacity. This paper presents a selection of identifiers employ Cepstrum parameters and the application of VQ speaker recognition system to improve a side France. When training for the amount of data is smaller, rehabilitation Cepstrum be relatively stable recognition performance. VQ improved ways to avoid the speech recognition system of training and the use of the difference in time, resulting in excessive sys Platform: |
Size: 23552 |
Author:张开 |
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Description: 说话人识别方法及其系统的应用开发研究.毕业设计论文,详细.本文对说话人识别方法应用作了较深入系统的研究。采用的方法分别是矢量量化(VQ)识别方法、隐马尔可夫模型(HMM)识别方法、高斯混合模型(GMM)识别方法。-Speaker Recognition Method and system development and research. Graduate design thesis in detail. In this paper, methods of application of speaker recognition system were made more in-depth research. Methods used are vector quantization (VQ) identification methods, hidden Markov model (HMM) to identify methods, Gaussian mixture model (GMM) identification methods. Platform: |
Size: 623616 |
Author:叶小勇 |
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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: 文中详细介绍了一种基于GMM 与 SVM的说话人识别系统。包括特征提取，算法实现及实验数据。对语音处理及说话人识别技术的研究者很有帮助。-Introduce a novel speake identification system based on GMM and SVM,with feature extract,algorithm research and experiment data.This doc would be helpful to those who are working on speech or speaker identification issue. Platform: |
Size: 608256 |
Author:张晓宇 |
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Description: 在MATLAB环境下实现基于矢量量化的说话人识别系统。在实时录音的情况下，利用该语音识别系统，对不同的人的语音进行辨识。实现与文本无关的自动说话人确认的实时识别。-In the MATLAB environment VQ-based Speaker Recognition System. In real-time recording, the use of the voice recognition system, for different people to carry out voice recognition. Implementation of automatic text-independent speaker verification of real-time identification. Platform: |
Size: 36864 |
Author:candy |
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Description: 使用GMM模式建立的语音识别系统，matlab源代码，供大家参考！-GMM model using the speech recognition system, matlab source code for your reference! Platform: |
Size: 1024 |
Author:王良 |
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Description: 识别正确率和抗噪性能是语音识别的研究重点，而识别响应速度也是决定系统实用化的关键 文章改进了传统的动态时间弯折算
法结构，将其应用于实时说话人辨识系统中，极大地提高了系统运行速度，随着待识别语音数目的增多，该算法优势更加明显 实验表明，
在不影响系统识别率的情况下，该方法使系统的运行速度平均提高了1.5 倍-Identify the correct rate and the anti-noise performance is the focus of speech recognition research, but also decided to identify the response speed of system utilities to improve the key article of the traditional structure of Dynamic Time Warping algorithm will be applied to real-time speaker identification system, greatly increase the speed of the system, with the question of an increase in the number of voice recognition, the algorithm is more obvious advantages of the experiment showed that the recognition rate does not affect the system' s case, the method of operation of the system increased the speed of an average of 1.5 times Platform: |
Size: 80896 |
Author:sdc |
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Description: 说话人识别和确认系统，采用matlab进行编写，能够进行说话人的识别和确认，研究声纹识别很好的参考代码-Speaker identification and verification system that uses matlab to write, to carry out the speaker' s identification and confirmation of a good reference voiceprint identification code Platform: |
Size: 228352 |
Author:smart |
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Description: 基于Matlab平台的语者识别系统
使用mfcc、DTW等算法-Matlab platform, based on speaker identification system using mfcc, DTW algorithm as Platform: |
Size: 3294208 |
Author:ida |
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Description: 用神经网络创建自适应的说话人识别系统的说明文章的-Help articles adaptive speaker identification system created using a neural network Platform: |
Size: 447488 |
Author:义恒 |
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Description: GMM／ANN混合说话人辨认模型 基于MATLAB平台的系统设计-GMM/ANN hybrid model speaker identification system design based on MATLAB Platform: |
Size: 153600 |
Author:温俊荣 |
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Description: Title: "Voice Recognition and Identification system".
Mainly involves:
1: Human speaker recognition
2: Technical data of samples
3: linear and logarithmic power spectrum plot
4: Plots for different values for N
5: Mel Space
6: Modified spectrum
7: 2D plot of accustic vectors
8: Plot of the 2D trained VQ codewords
9: Recognition rate of the computer
and so on-Title: "Voice Recognition and Identification system".
Mainly involves:
1: Human speaker recognition
2: Technical data of samples
3: linear and logarithmic power spectrum plot
4: Plots for different values for N
5: Mel Space
6: Modified spectrum
7: 2D plot of accustic vectors
8: Plot of the 2D trained VQ codewords
9: Recognition rate of the computer
and so on..... Platform: |
Size: 309248 |
Author:atish |
Hits:
Description: Automatic speaker-identification (SID) has long been an important research topic.
It is aimed at identifying who among a set of enrolled persons spoke a given
utterance. This study extends the conventional SID problem to examining if an SID
system trained using speech data can identify the singing voices of the enrolled
persons. Our experiment found that a standard SID system fails to identify most
singing data, due to the significant differences between singing and speaking for a
majority of people. In order for an SID system to handle both speech and singing
data, we examine the feasibility of using model-adaptation strategy to enhance the
generalization of a standard SID. Our experiments show that a majority of the
singing clips can be correctly identified after adapting speech-derived voice models Platform: |
Size: 1562624 |
Author:nagwa |
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Description: In this paper, we figure out the use of appended jitter and shimmer speech features for closed set text
independent speaker identification system. Jitter and shimmer features are extracted from the
fundamental frequency contour and added to baseline spectral features, specifically Mel-frequency
Cepstral Coefficients (MFCCs) for human speech and MFCC-GC which integrate the Gammachirp
filterbank instead of the Mel scale. Hidden Markov Models (HMMs) with Gaussian Mixture Models
(GMMs) state distributions are used for classification. Our approach achieves substantial performance
improvement in a speaker identification task compared with a state-of-the-art robust front-end in a
clean condition. Platform: |
Size: 256000 |
Author:mansouri |
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Description: In this work, the Mel frequency Cepstrum Coefficient
(MFCC) feature has been used for designing a text
dependent speaker identification system. The extracted
speech features (MFCC’s) of a speaker are quantized to a
number of centroids using vector quantization algorithm.
These centroids constitute the codebook of that speaker.
MFCC’s are calculated in training phase and again in testing
phase. Speakers uttered same words once in a training
session and once in a testing session later. The Euclidean
distance between the MFCC’s of each speaker in training
phase to the centroids of individual speaker in testing phase
is measured and the speaker is identified according to the
minimum Euclidean distance. The code is developed in the
MATLAB environment and performs the identification
satisfactorily. Platform: |
Size: 1024 |
Author:abdou |
Hits:
Search - speaker identification system