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: 23925 |
Author:张开 |
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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: VQ声纹识别算法和实验.
摘要：采用线性预测倒谱系数(1inear prediction cepstrum coefficient，LPCC)作为语音的特征参数，矢量量化(vector quantity，VQ)方法进行模式匹配，探讨声纹识别以实现身份认证，并对此识别方法进行了相关的实验．通过验证，这种方法可以区分不同的说话人，并且在做说话人辨认实验时可达到较高的识别率．-VQ pattern recognition algorithms and experimental sound. Abstract: The linear prediction cepstral coefficients (1inear prediction cepstrum coefficient, LPCC) as the voice of the characteristic parameters, vector quantization (vector quantity, VQ) method of pattern matching, pattern recognition of sound in order to achieve authentication and identification methods relevant to this experiment. Validated, this method can distinguish between different speaker, and speaker identification experiments to do to reach a higher recognition rate. Platform: |
Size: 171008 |
Author:海边贝壳 |
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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 |
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Search - speaker identification cepstrum