Optimum MVF Estimation‐Based Two‐Band Excitation for HMM‐Based Speech Synthesis

The optimum maximum voiced frequency (MVF) estimation‐based two‐band excitation for hidden Markov model‐based speech synthesis is presented. An analysis‐by‐synthesis scheme is adopted for the MVF estimation which leads to the minimum spectral distortion of synthesized speech. Experimental results show that the proposed method significantly improves synthetic speech quality.     

基于隐马尔可夫模型的网络状态分析方法

针对数据过滤问题提出了一种网络状态分析方法,该方法从恶意数据流中提取二维特征,经过数值加权把这二维特征整合为观测数据,建立了一个对恶意数据流敏感的隐马尔可夫模型,最后以各种网络数据集为例对其应用情况进行了分析。     

基于规则的多模型集成化异常入侵检测系统

在讨论相关技术的基础上,提出一种多模型集成的入侵检测系统,然后运用不同的模型和方法,构建系统的正常行为,并将其使用基于规则的方法集成,实验证明系统的检测率得到提高的同时,大大降低了误警率.     

基于HMM和Monte-Carlo方法的生成基频曲线的新方法

提出了一种基于隐马尔科夫模型(HMM)的汉语韵律短语的统计基频模型,并尝试使用蒙特卡罗方法(Monte-Carlo)从模型生成基频曲线。该模型使用HMM作为基本框架,具有HMM理论体系所能支配的各种优点。并将韵律词作为模型单元,使得模型能够反映韵律层次级的连续变调。Monte-Carlo方法能生成相对模型最优的基频曲线,并保证生成的基频曲线具有足够的精确度。该文最后给出了实验结果并对模型的应用前景进行了展望。     

Speech Recognition-Based Automated Visual Acuity Testing with Adaptive Mel Filter Bank

One of the most commonly reported disabilities is vision loss, which can be diagnosed by an ophthalmologist in order to determine the visual system of a patient. This procedure, however, usually requires an appointment with an ophthalmologist, which is both time-consuming and expensive process. Other issues that can arise include a lack of appropriate equipment and trained practitioners, especially in rural areas. Centered on a cognitively motivated attribute extraction and speech recognition approach, this paper proposes a novel idea that immediately determines the eyesight deficiency. The proposed system uses an adaptive filter bank with weighted mel frequency cepstral coefficients for feature extraction. The adaptive filter bank implementation is inspired by the principle of spectrum sensing in cognitive radio that is aware of its environment and adapts to statistical variations in the input stimuli by learning from the environment. Comparative performance evaluation demonstrates the potential of our automated visual acuity test method to achieve comparable results to the clinical ground truth, established by the expert ophthalmologist’s tests. The overall accuracy achieved by the proposed model when compared with the expert ophthalmologist test is 91.875%. The proposed method potentially offers a second opinion to ophthalmologists, and serves as a cost-effective pre-screening test to predict eyesight loss at an early stage.     

A Hybrid Intelligent Approach for Content Authentication and Tampering Detection of Arabic Text Transmitted via Internet

In this paper, a hybrid intelligent text zero-watermarking approach has been proposed by integrating text zero-watermarking and hidden Markov model as natural language processing techniques for the content authentication and tampering detection of Arabic text contents. The proposed approach known as Second order of Alphanumeric Mechanism of Markov model and Zero-Watermarking Approach (SAMMZWA). Second level order of alphanumeric mechanism based on hidden Markov model is integrated with text zero-watermarking techniques to improve the overall performance and tampering detection accuracy of the proposed approach. The SAMMZWA approach embeds and detects the watermark logically without altering the original text document. The extracted features are used as a watermark information and integrated with digital zero-watermarking techniques. To detect eventual tampering, SAMMZWA has been implemented and validated with attacked Arabic text. Experiments were performed on four datasets of varying lengths under multiple random locations of insertion, reorder and deletion attacks. The experimental results show that our method is more sensitive for all kinds of tampering attacks with high level accuracy of tampering detection than compared methods.     

OFDM系统的受限隐马尔可夫模型

本文采用隐马尔可夫模型(HMM)的一种特例--受限隐马尔可夫模型(CHMM)对正交频分复用(OFDM)系统进行建模,给出了CHMM参数估计算法的改进,可避免计算机编程实现时出现下溢导致的计算失败.通过仿真讨论了CHMM状态数的选取问题,并给出了HIPERLAN/2室内无线信道环境下OFDM系统的一组CHMM,该模型可用于比较不同编码方案的性能或者分析高层协议性能.     

最大互信息用于语音识别

将最大互信息理论用于语音识别,最大互信息估计作为目标函数。在隐马尔可夫模型参数调整过程中运用了泛化概率下降方法,保证了统计意义上实现目标函数的优化。最大互信息估计用于连接数字语音识别,识别率得到了提高。     

Improving the performance of focused web crawlers

This work addresses issues related to the design and implementation of focused crawlers. Several variants of state-of-the-art crawlers relying on web page content and link information for estimating the relevance of web pages to a given topic are proposed. Particular emphasis is given to crawlers capable of learning not only the content of relevant pages (as classic crawlers do) but also paths leading to relevant pages. A novel learning crawler inspired by a previously proposed Hidden Markov Model (HMM) crawler is described as well. The crawlers have been implemented using the same baseline implementation (only the priority assignment function differs in each crawler) providing an unbiased evaluation framework for a comparative analysis of their performance. All crawlers achieve their maximum performance when a combination of web page content and (link) anchor text is used for assigning download priorities to web pages. Furthermore, the new HMM crawler improved the performance of the original HMM crawler and also outperforms classic focused crawlers in searching for specialized topics.     

改进的XML智能数据清洗策略

摘要：针对XML数据的质量问题,以XML键为基础,借助多模板隐马尔可夫模型信息抽取策略与粒子群优化算法构建新的XML数据清洗方法。为了提高XML相似性数据并行检测效率,利用波函数对粒子群优化算法进行优化。仿真实验表明,与其他XML数据清洗算法相比,该方法的白适应学习能力强、人工参与程度低、计算量小,时间性能有94％左右的提升。