针对多种处理痕迹的数字语音取证算法

现有的数字语音取证研究主要集中于对单一的某种操作进行检测，无法对不相关的操作进行判断。针对该问题，提出了一种能够同时检测经过变调、低通滤波、高通滤波和加噪这四种操作的数字语音取证方法。首先，计算语音的归一化梅尔频率倒谱系数（MFCC）统计矩特征；然后通过多个二分类器对特征进行训练，并组合投票得到多分类器；最后使用该多分类器对待测语音进行分类。在TIMIT以及UME语音库上的实验结果表明，归一化MFCC统计矩特征在库内实验中均达到了97%以上的检测率，且在对MP3压缩鲁棒性测试的实验中，检测率仍能保持在96%以上。     

Institutional diversity and safe drinking water provision in the United States

In the United States, community water systems are not only numerous but also diverse. Nonetheless, operational scale and public versus private ownership have dominated the understanding of safe water access, leaving underexplored many other factors related to governance. Applying insights from the Institutional Analysis and Design literature, we create and analyze a dataset of California's water systems classified into 26 structural types associated with distinct governance institutions. We document differences in the number of Safe Drinking Water Act violations among these types and demonstrate their relevance for implementing solutions targeting non-compliance. Advancing equitable access necessitates greater attention to organizational structure and institutional diversity in drinking water provision.     

Flexible Piezoelectric Acoustic Sensors and Machine Learning for Speech Processing

Flexible piezoelectric acoustic sensors have been developed to generate multiple sound signals with high sensitivity, shifting the paradigm of future voice technologies. Speech recognition based on advanced acoustic sensors and optimized machine learning software will play an innovative interface for artificial intelligence (AI) services. Collaboration and novel approaches between both smart sensors and speech algorithms should be attempted to realize a hyperconnected society, which can offer personalized services such as biometric authentication, AI secretaries, and home appliances. Here, representative developments in speech recognition are reviewed in terms of flexible piezoelectric materials, self-powered sensors, machine learning algorithms, and speaker recognition.     

Combination of dynamic features with a new mask to optimize neural network speech enhancement

Concerning the problem that the Neural Network speech enhancement algorithm cannot fully represent the nonlinear structure of speech due to feature selection,which leads to speech distortion.This paper proposes the combination of dynamic features with a new mask to optimize neural network speech enhancement.First,three features of noisy speech are extracted and spliced to obtain static features.Then,the first and second difference derivatives are obtained to capture the instantaneous signals of speech and fuse them into dynamic features.The combination of dynamic and static features completes internal complementarity of features and reduced speech distortion.Second,in order to enhance the intelligibility and clarity of speech at the same time,an adaptive mask is proposed,which can adjust the energy ratio of speech and noise as well as the ratio of the traditional mask and the square root mask.The Gammatone channel weight is used to modify the mask value in each channel to simulate the human auditory system and further improve the speech intelligibility.Finally,the simulation of multiple voices under different noise backgrounds shows that compared with different literature algorithms,the algorithm has a higher SNR,subjective speech quality and short-term objective intelligibility,which verifies the effectiveness of the algorithm.     

一种适用于井下通信的自适应滤波器研究

针对煤矿井下严重的环境噪声影响，文章论述了一种受话音频谱控制的自适应动态滤波器。它可以有效地减小井下环境噪声对话音信号的影响，达到改善通信质量，增加通信距离的效果。     

无形文化遗产的异地保护与开发——以湖南大崇山文化生态园规划为例

通过对无形文化遗产异地保护必要性的理解，对无形文化遗产保护与旅游开发相结合进行了一种新的尝试。     

Reasons for the Importance of Politeness

礼貌在大多数人看来，意味着好的礼节，得体的言谈举止。实际上礼貌所涉及的问题非常深奥、复杂。本文在分析礼貌内涵的基础上，从面子需求以及文化观念二方面详细阐述了礼貌用语重要性的原因。     

高质量散布脉冲CELP算法的定点DSP实时实现

为了将低比特率语音编码算法应用于实际的语音处理系统,开发了一种基于TMS320VC5410定点DSP 的实时低速率语音压缩系统．语音编码方法采用一种高质量的4 kb／s散布脉冲CELP算法．用汇编语言编写的定点DSP程序能在目标平台上实时完成语音数据的采集、处理和回放功能．整个系统大约占用31 K存储空间, 算法复杂度达到指令数为5．42×107条／s．客观质量测试结果显示,DSP定点程序处理后的语音质量非常接近于浮点C语言处理后的结果,表明该语音处理系统能满足实时语音压缩的需求．     

Study on Unequal Error Protection for Distributed Speech Recognition System

At present ,the trend to ever-increasing use ofdata communication is spreading to the mobile wire-less world. The small portable devices will be used toaccess these data and cry out for i mproved user inter-faces using speechinput , whichis very i mportan…     

Data-Driven Temporal Filtering on Teager Energy Time Trajectory for Robust Speech Recognition

Data-driven temporal filtering technique is integrated into the time trajectory of Teager energy operation （TEO） based feature parameter for improving the robustness of speech recognition system against noise. Three kinds of data-driven temporal filters are investigated for the motivation of alleviating the harmful effects that the environmental factors have on the speech. The filters include： principle component analysis （PCA） based filters, linear discriminant analysis （LDA） based filters and minimum classification error （MCE） based filters. Detailed comparative analysis among these temporal filtering approaches applied in Teager energy domain is presented. It is shown that while all of them can improve the recognition performance of the original TEO based feature parameter in adverse environment, MCE based temporal filtering can provide the lowest error rate as SNR decreases than any other algorithms.