异常检测算法分析

介绍了异常检测的算法，如基于统计的异常检测算法，基于深度的检测算法等，并重点分析了近些年来的一些新发展，如基于距离的异常检测，基于密度的异常检测以及面向高维数据的异常检测，并优缺点。     

基于BP神经网络的高速公路交通事件检测

回顾了几种传统的交通事件检测算法，提出基于多层前向人工神经网络的检测模型，并运用BP算法予以实现。在将BP算法与传统算法进行比较之后，发现BP算法具有检测率高、误报率低、平均检测时间短的优点，同时也存在不足之处，指出了今后进一步研究的方向。     

一种特洛伊木马的检测算法

运用字母集合到数字集合的映射理论、字符串的可计算性理论与数论理论，设计了一种特洛伊木马的检测算法，该算法能够有效地检测出特洛伊木马，同已有的检测算法相比，速度较快，不但适合字母文字的可执行程序检测，也适合可能出现的非字母文字所编写的可执行程序的特洛伊木马检测．     

快速模糊边缘检测算法

通过对Pal.King的模糊边缘检测算法进行改进，提出了一种快速模糊边缘检测算法，该快速算法不但简化了Pal.King算法中复杂的G和G^-1运算，而且通过实验，确定了Tr变换中最佳的隶属度阈值，大大地减少了迭代次数，从实验结果中可以看出，该快速算法不但提高了PalKing算法的效率，而且具有很强的检测模糊边缘和细小边缘的能力，这种快速算法的性能优越，是一种非常实用的、高效的图像处理算法。     

一种用于内容过滤和检测的快速多关键词识别算法

基于字符串匹配的检测方法是内容过滤和检测系统中一类很重要的分析方法，首先分析了现有的几种快速字符串匹配算法，然后提出了一种新的多模式字符串匹配算法，并简单分析了算法的复杂性，算法在设计的过程中吸取了BM算法中跳跃的特性，采用了后缀树算法得到了最大跳跃值，采用AC算法的匹配自动机原理从而避免对搜索树内每一个字符的匹配，最后，通过具体的实验数据验证了这些算法的性能，通过实验可以看出，新算法使得检测速度有很大提高，并有效屏蔽了关键词数量的增加对检测速度的影响。     

Canny边缘检测算法在颗粒边缘检测方面的应用

该文通过对Canny边缘检测算法的第一步进行改进的，即用一种自适应的高斯滤波器替代传统滤波器对图像进行预处理。然后应用在颗粒边缘检测方面，实验结果证明，与传统Canny算法相比，改进的算法在抗噪声干扰和精确定位等方面都取得了比较好的效果。     

基于数字图像处理的表面裂纹检测算法

本文提出了一种基于数字图像处理技术的表面裂纹检测算法。运用这种算法能精确的检测裂纹的位置、长度等特征，将这种裂纹检测算法运用到裂纹自动检测系统以及裂纹扩展行为监测中不仅大大降低了劳动时间和劳动强度，而且提高了测量的准确性。     

基于运动信息的镜头切变检测算法

镜头检测算法的研究是视频分析与检索中较为活跃的研究领域之一。该文提出了一种基于运动信息的镜头切变检测算法，该算法采用四叉树算法分析视频序列的运动信息，进而提取基于运动信息的特征矢量，由提取的特征矢量可以检测出视频序列中的镜头切变点。实验表明该算法是有效的，可行的，并且具有非常强的抗干扰能力。     

入侵检测系统中的快速多模式匹配算法

网络入侵检测系统常常依赖于精确的模式匹配技术，依赖于算法的选择、实现以及使用频率。这种模式匹配技术可能成为入侵检测系统的瓶颈，为了跟上快速增长的网络速度和网络流量，Snort(开放源代码的网络入侵检测系统)中采用了快速多模式匹配算法，本文描述了Snort中一种引入注目的快速多模式匹配算法及其对系统性能的改进。     

聚类算法在网络入侵检测中的应用

分析了目前的入侵检测技术，提出了使用聚类算法进行网络入侵检测的方法，并通过试验说明了该方法的应用效果。     

语音转换关键技术研究

语音转换技术在语音处理领域是一个比较新的研究方向,也是近年来语音领域的研究热点。语音转换技术是指改变源说话人的语音特征使之具有目标说话人特征的一项技术。本文说明了语音转换的定义,介绍了语音的个性特征,列举了频谱包络的几种主要的转换算法以及韵律转换的主要算法。最后说明了语音转换今后的研究方向。     

一种快速嘴部检测方法在视听语音识别的应用

在改进噪音环境下的语音识别率中,来自于说话人嘴部的可视化语音信息有着显著的作用。介绍了在视听语音识别（AVSR）中的重要组成部分之一：可视化信息的前端设计;描述了一种用于快速处理图像并能达到较高识别率的人脸嘴部检测的机器学习方法,此方法引入了旋转Hart-like特征在积分图像中的应用,在基于AdaBoost学习算法上通过使用单值分类作为基础特征分类器,以级联的方式合并强分类器,最后划分检测区域用于嘴部定位。将上述方法应用于AVSR系统中,基本上达到了对人脸嘴部实时准确的检测效果。     

一种基于遗传算法的话者身份确认系统建模方法

本文描述了一种采用短语和基于遗传算法的话者身份确认系统的建模方法,利用遗传算法的全局搜索和优化特性,系统只需利用话者的短语音,就可快速建立话者的一类较优秀的模板。实验结果表明,这种方法既降低了用户的语音数据采集量,有利于话者模板的建立,又提高了系统的确认性能及鲁棒性,较传统方法有明显的优越性。     

Visible speech improves human language understanding: Implications for speech processing systems

Evidence from the study of human language understanding is presented suggesting that our ability to perceive visible speech can greatly influence our ability to understand and remember spoken language. A view of the speaker's face can greatly aid in the perception of ambiguous or noisy speech and can aid cognitive processing of speech leading to better understanding and recall. Some of these effects have been replicated using computer synthesized visual and auditory speech. Thus, it appears that when giving an interface a voice, it may be best to give it a face too.     

Automatic Detection of Disfluency Boundaries in Spontaneous Speech of Children Using Audio–Visual Information

The presence of disfluencies in spontaneous speech, while poses a challenge for robust automatic recognition, also offers means for gaining additional insights into understanding a speaker's communicative and cognitive state. This paper analyzes disfluencies in children's spontaneous speech, in the context of spoken dialog based computer game play, and addresses the automatic detection of disfluency boundaries. Although several approaches have been proposed to detect disfluencies in speech, relatively little work has been done to utilize visual information to improve the performance and robustness of the disfluency detection system. This paper describes the use of visual information along with prosodic and language information to detect the presence of disfluencies in a child's computer-directed speech and shows how these information sources can be integrated to increase the overall information available for disfluency detection. The experimental results on our children's multimodal dialog corpus indicate that disfluency detection accuracy of over 80% can be obtained by utilizing audio-visual information. Specifically, results showed that the addition of visual information to prosody and language features yield relative improvements in disfluency detection error rates of 3.6% and 6.3%, respectively, for information fusion at the feature level and decision level.     

Idiolect Extraction and Generation for Personalized Speaking Style Modeling

A person's speaking style, consisting of such attributes as voice, choice of vocabulary, and the physical motions employed, not only expresses the speaker's identity but also emphasizes the content of an utterance. Speech combining these aspects of speaking style becomes more vivid and expressive to listeners. Recent research on speaking style modeling has paid more attention to speech signal processing. This approach focuses on text processing for idiolect extraction and generation to model a specific person's speaking style for the application of text-to-speech (TTS) conversion. The first stage of this study adopts a statistical method to automatically detect the candidate idiolects from a personalized, transcribed speech corpus. Based on the categorization of the detected candidate idiolects, superfluous idiolects are extracted using the fluency measure while the remaining candidates are regarded as the nonsuperfluous idiolects. In idiolect generation, the input text is converted into a target text with a particular speaker's speaking style via the insertion of superfluous idiolect or synonym substitution of nonsuperfluous idiolect. To evaluate the performance of the proposed methods, experiments were conducted on a Chinese corpus collected and transcribed from the speech files of three Taiwanese politicians. The results show that the proposed method can effectively convert a source text into a target text with a personalized speaking style.     

低码率语音编码MELP的SOPC实现

讨论了低码率语音编码MELP的编解码过程,有效降低了语音编码码率并能使说话者个人语音特征减弱,特别适合需要弱化说话者语音特点的场合.给出了其FPGA的硬件实现框图,据此可进行具体的硬件设计.同时给出了MELP编解码框图,可用于进一步的软件编制.     

基于关键点表示的语音驱动说话人脸视频生成

针对现有语音生成说话人脸视频方法忽略说话人头部运动的问题,提出基于关键点表示的语音驱动说话人脸视频生成方法.分别利用人脸的面部轮廓关键点和唇部关键点表示说话人的头部运动信息和唇部运动信息,通过并行多分支网络将输入语音转换到人脸关键点,通过连续的唇部关键点和头部关键点序列及模板图像最终生成面部人脸视频.定量和定性实验表明,文中方法能合成清晰、自然、带有头部动作的说话人脸视频,性能指标较优.     

基于PCNN的语谱图特征提取在说话人识别中的应用

该文首次提出了一种将有生物视觉依据的人工神经网络——脉冲耦合神经网络(PulseCoupledNeuralNetwork,以下简称为PCNN)用于说话人识别领域的语谱图特征提取的新方法。该方法将语谱图输入到PCNN后得到输出图像的时间序列及其熵序列作为说话人语音的特征,利用它的不变性实现说话人识别。实验结果表明,该方法可以快速有效地进行说话人识别。该文将PCNN引入到语音识别的应用研究中,开拓了信号处理中两个极为重要的部分———语音信号处理和图像信号处理结合的新领域,同时对于PCNN的理论研究和实际应用具有非常重要的现实意义。     

Study on processing of wavelet speech denoising in speech recognition system

The development of society promotes the continuous progress of science and technology, and speech processing technology gradually occupies an increasingly important position in people’s life and work, which puts forward higher requirements on the speech processing technology, especially in noisy environment. Due to the complexity of the real environment, denoising processing has great practical significance. In order to improve the level of speech denoising and increase the accuracy of the speech recognition system, wavelet denoising technology was used to analyze the de-noising requirements and hard and soft threshold functions in the speech recognition system, and an improved wavelet threshold denoising algorithm was put forward. Firstly, the signals were processed by wavelet decomposition according to primary function; then denoising was performed using the improved function; finally the denoised signals were reconstructed using inverse operation. The denoising effect of the algorithm was verified. The results showed that it was effective in denoising conventional speech signals. Besides, it was applied to the speech recognition system to denoise the noisy speech collected in the real environment, and finally high system self-assessment parameters were obtained. Thus it is concluded that wavelet denoising is effective in the speech denoising of the speech recognition system and can be put into practice.