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排序方式: 共有44条查询结果,搜索用时 0 毫秒
1.
提出了一种基于梅尔频率倒谱系数相关性的语音感知哈希内容认证算法。该算法提取分段语音的声纹梅尔频率倒谱系数作为感知特征。为提高算法的安全性,算法利用伪随机序列作为密钥,计算得到梅尔频率倒谱系数与伪随机之间的相关度,最后量化相关值并加密生成感知哈希序列。语音认证过程中,采用相似性度量函数用来衡量哈希序列之间的距离,同时与汉明距离方法进行了比较。仿真结果表明,该算法对语音内容保持操作,如重采样、MP3压缩等具有较好的鲁棒性,相似性度量函数也对语音篡改检测定位具有较高的灵敏性。  相似文献   
2.
为了便于人们在陌生的公共场所更加方便、快捷地找到指定地点,设计并实现了公共场所智能语音交互引导系统。首先根据场所的结构布局,利用Dijkstra算法求出最短路径,再结合语音识别的原理,提取语音特征,采用动态时间规整算法与模板进行相识匹配,实现用语音交互的方式,引导使用者到达指定地点。本系统的软件及操作界面使用MATLAB软件平台设计,经过测试,本系统在复杂的室内环境下,可以直观、快捷地实现语音交互方式下的地图导航功能,语音识别率达到87.75%。  相似文献   
3.
In a recent study, we have introduced the problem of identifying cell-phones using recorded speech and shown that speech signals convey information about the source device, making it possible to identify the source with some accuracy. In this paper, we consider recognizing source cell-phone microphones using non-speech segments of recorded speech. Taking an information-theoretic approach, we use Gaussian Mixture Model (GMM) trained with maximum mutual information (MMI) to represent device-specific features. Experimental results using Mel-frequency and linear frequency cepstral coefficients (MFCC and LFCC) show that features extracted from the non-speech segments of speech contain higher mutual information and yield higher recognition rates than those from speech portions or the whole utterance. Identification rate improves from 96.42% to 98.39% and equal error rate (EER) reduces from 1.20% to 0.47% when non-speech parts are used to extract features. Recognition results are provided with classical GMM trained both with maximum likelihood (ML) and maximum mutual information (MMI) criteria, as well as support vector machines (SVMs). Identification under additive noise case is also considered and it is shown that identification rates reduces dramatically in case of additive noise.  相似文献   
4.
基于语音信号的频谱特性,本文对说话人识别技术中Mel倒谱参数做了改进,并通过Microsoft Visual C 6.0验证了在低信噪比时使用改进后的Mel倒谱参数可以提高说话人识别系统的正确识别率.  相似文献   
5.
语音情感识别是人工智能的重要研究领域之一,特征参数提取的准确性直接影响识别的效果。分析了发音持续时间、平均振幅、基音频率,第一共振峰和Mel频率倒谱参数,并基于模糊熵理论提取了各参数的权重。再利用模糊熵进行有效的度量融合.最后通过改进后综合判决对情感语句做出识别判定。研究发现融合后的参数增强了情感识别的效果。  相似文献   
6.
文章在对应力影响下变异语音进行分频带分析的基础上,选用了可以提升语音信号中频段影响的修正Mel频率映射,并将其对应的MFCC系数作为新的语音识别特征。通过采用正常/变异语音分类器和新特征来进行变异语音识别。实验结果表明:采用修正Mel频率映射的MFCC特征改进了变异语音的识别性能。  相似文献   
7.
快速准确地检测出采集录音中的咳嗽部分对许多呼吸道疾病的临床诊断有着重要意义。使用梅尔频率倒谱系数(MFCC)作为特征参数来分析所要处理的声音信号,并用多组训练数据分别为采集录音中的咳嗽音、说话声、笑声、清喉音等数据各建立两个高斯混合模型(GMM),将每类数据得到的两个GMM进行线性组合得到最终的表示每类数据的概率模型,进而实现对咳嗽音部分的检测。在此基础上引入了小波去噪理论,分别对每段数据去噪并进行端点检测。仿真实验结果表明所提方法能够有效提高系统的识别性能。  相似文献   
8.
提出了一种基于高斯混合模型(GMM)的自然环境声音的识别方法。提取Mel频率倒谱系数(MFCCs)来分析声音信号;对于每种声音使用期望最大化算法基于MFCC特征集建立高斯混合模型;使用最小错误率判决规则和投票裁决的方法进行识别。使用GMM对36种自然环境的声音进行识别的正确率可达95.83%,且识别效果优于K最近邻(KNN)。  相似文献   
9.
语音信号时频特征显示系统的设计和仿真   总被引:1,自引:0,他引:1  
语音信号处理算法众多,但用于语音处理算法验证和开发的可视化研究平台极少。基于MATLAB GUI技术,完成语音信号典型时频特征参数提取和显示系统仿真平台的设计。可实现多种格式音频文件的载入和播放、波形和频谱显示、以及线性预测倒谱系数和美尔倒谱系数的计算、存储和显示等功能。系统界面友好、操作方便,可实现参数的交互输入并控制显示结果。仿真结果验证了相关时频特征参数提取算法的正确性,提高了对算法或数据处理效果的直观认识。  相似文献   
10.
Automatic recognition of the speech of children is a challenging topic in computer-based speech recognition systems. Conventional feature extraction method namely Mel-frequency cepstral coefficient (MFCC) is not efficient for children's speech recognition. This paper proposes a novel fuzzy-based discriminative feature representation to address the recognition of Malay vowels uttered by children. Considering the age-dependent variational acoustical speech parameters, performance of the automatic speech recognition (ASR) systems degrades in recognition of children's speech. To solve this problem, this study addresses representation of relevant and discriminative features for children's speech recognition. The addressed methods include extraction of MFCC with narrower filter bank followed by a fuzzy-based feature selection method. The proposed feature selection provides relevant, discriminative, and complementary features. For this purpose, conflicting objective functions for measuring the goodness of the features have to be fulfilled. To this end, fuzzy formulation of the problem and fuzzy aggregation of the objectives are used to address uncertainties involved with the problem.The proposed method can diminish the dimensionality without compromising the speech recognition rate. To assess the capability of the proposed method, the study analyzed six Malay vowels from the recording of 360 children, ages 7 to 12. Upon extracting the features, two well-known classification methods, namely, MLP and HMM, were employed for the speech recognition task. Optimal parameter adjustment was performed for each classifier to adapt them for the experiments. The experiments were conducted based on a speaker-independent manner. The proposed method performed better than the conventional MFCC and a number of conventional feature selection methods in the children speech recognition task. The fuzzy-based feature selection allowed the flexible selection of the MFCCs with the best discriminative ability to enhance the difference between the vowel classes.  相似文献   
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