基于双哈希索引的高效语音生物哈希安全检索算法

针对语音数据在信道传输与云端存储时的安全性问题,以及由于语音数据数目大、维数高、空间复杂度高带来的检索效率问题,提出了一种基于双哈希索引的高效语音生物哈希安全检索算法。首先,在服务端分别提取语音信号的频谱通量与峭度因子特征并将两种特征融合,利用Bagging分类对语音信号的差分哈希分类,并基于分类结果构建密钥分配索引表;然后,根据密钥分配索引表建立具有单一映射密钥的生物特征模板,并将其量化构造生物哈希,得到哈希索引;同时,采用混合域置乱加密算法对原始语音加密,构建密文语音库;最后,将哈希索引与密文语音库上传至云端并构建云端生物哈希索引表。在移动端,采用归一化汉明距离进行匹配检索。实验结果表明:本文算法的匹配阈值区间为(0.2694,0.4173),说明该检索算法能够灵活选取匹配阈值,具有较好的鲁棒性和区分性;检索过程中单条语音平均检索时间仅为9.4957×10^-4s,并且经过15种内容保持操作后的查全率与查准率均为100%,说明该算法具有较好的检索性能,可以满足各种环境下的语音检索需求;同时提出的加密算法密钥空间大小为10⁶⁰,说明能够抵御穷举密钥攻击、保证语音数据的安全;此外,构建的生物特征模板具有良好的多样性、安全性和可撤销性。     

集群环境下分布式索引的实现

针对分布式存储系统上使用非主键访问数据带来的性能问题,探讨在分布式存储系统上实现索引的相关关键技术。在充分分析分布式存储特征的基础上,提出了分布式索引设计和实现的关键点,并结合分布式存储系统的特点及相关的索引技术,讨论了索引的组织形式、索引的维护和数据一致性等问题;然后基于如上的分析,选择在分布式数据库系统OceanBase开源版本上,设计和实现分布式索引机制,并通过基准测试工具YCSB进行性能测试。实验结果表明,虽然辅助索引会对系统性能产生影响,但因为充分考虑了系统特征及存储特点,在不同数据规模下,该索引都能够将性能影响控制在5%以内。另外,使用冗余列的方式,能进一步将该索引的性能提升100%。     

一种融合音位属性的语音文档索引方法

为提高索引覆盖率并获得更多的候选路径,提出一种在词格上融合音位属性的语音文档索引方法.通过基于音位属性检测的语音识别系统建立词格,利用其信息互补性,与传统的词格进行起止节点合并.针对合并后Lattice规模增大的问题,采用基于位置的分段对齐方法对其结构进行压缩.实验结果表明,该方法在提高索引覆盖率和降低最小错误率方面均优于传统的语音文档索引方法,能够有效提高语音检索性能.     

一种基于网格与R树的多级混合索引

结合网格索引和R树索引的特点,提出了一种基于网格与R树的多级混合索引.该方案首先将矩形地理空间进行粗网格划分建立多级网格索引.然后针对每个小网格建立基于R树的空间索引.详细讨论了该索引的结构、建立算法、删除算法以及应用该索引的检索算法,并进行了算法分析.与网格索引和R树索引相比,该索引以略大的空间开销换取了更高的查找性能.     

基于格的汉语自然对话语音索引方法研究

对汉语自然对话语音索引问题进行了研究. 比较了不同单元格的识别和检索性能, 提出不同单元格的转换方法、格间的融合方法以及格内节点与边的合并方法. 格转换实现了识别单元和索引单元的分离, 词格转换得到的无调音节格将品质因数(Figure of merit, FOM)从基线系统的69.2%提高到73.7%; 格间融合综合利用多个格的信息, 将FOM进一步提高到78.6%; 格内合并对格进行了有效的压缩, 使其可应用于海量语音检索.     

一种改进的XML压缩树索引技术

压缩树索引技术是XML数据压缩的热点问题之一,本文提出一种压缩树索引改进方法.针对压缩树在查询过程中不能很好的解决向上匹配与向下匹配的问题,改进方法引入正排索引和倒排索引.当查询到组一级时,利用正排索引可以快速的查找出以该组为父节点的子组.而选出符合值谓词的元素后,在进行向上匹配时利用倒排索引可找出该元素的父节点.新的索引方法在保留原压缩树索引优点的基础上,解决了压缩树索引在查询过程中匹配问题.     

一种基于动态平衡树的在线索引快速构建方法

倒排索引的构建可以通过离线方式高效地完成,但是仅当整个数据集索引完毕后方可提供检索服务.在线索引可以在构建倒排索引的同时提供检索服务,新加入的文档即刻可供检索.提出了一种基于动态平衡树的在线索引更新策略,利用动态平衡树控制索引合并过程,使索引合并总是在大小相近的子索引之间进行,以减少索引合并代价,同时可以调节索引和检索之间的性能平衡.该方法提供了一个基于合并的在线索引更新框架,与已有方法相比具有更好的通用性、更高的性能和更好的规模可扩展性.在由4000万张网页构成的270 GB Web数据集上运行的实验表明,该方法在实际系统中是高效的,将索引更新的性能提高了92.28%,而检索性能仅下降4.79%,大幅度降低了在线索引构建的代价.     

一种高效的全文检索索引技术*

针对目前比较流行的基于词的倒排文档索引模型,结合全文检索数据的特点,提出了变长编码的索引压缩算法。利用该压缩编码,研究了基于内存缓存的快速创建索引的流程。通过实验,对索引膨胀率、创建时间和检索响应速度进行了对比分析,表明该技术提高了索引的空间与时间效率。     

基于多线程并行强化学习的数据库索引推荐

建立索引是提高数据库性能的一个重要方法。目前随着强化学习算法的发展,出现了一系列使用强化学习解决索引推荐问题（index selection problem,ISP）的方法。针对现有的深度强化学习索引推荐算法训练时间长,训练不够稳定的问题,提出了一个基于A2C的索引推荐算法PRELIA。该算法加入负载索引扫描行数特征矩阵,并对奖励值进行归一化处理,旨在提高索引选择的准确性和效率,减少索引空间占用。在不同数据集上的实验结果表示,该算法可以在保证与比较的算法相当的索引推荐质量同时,推荐出的索引占用更小的存储空间,同时训练时间比基线算法时间提高了4倍以上。     

基于多层空间模糊减法聚类算法的Web数据库安全索引

目前对Web数据库进行索引查询时采用单层文本特征聚类方法,当聚类特征不一致时,存在着非法聚类和非法结果输出的安全问题。提出一种基于多层空间模糊减法聚类的Web数据库安全索引算法,该算法将数据库信息矢量构建成多层矢量自回归空间,把数据流信息聚焦在空间的多层空间模糊聚类中心,采用减法聚类的模糊推理方法构建数据库索引函数,变尺度调整聚类中心向量,搜索索引结果,阻止了邻近数据点非法侵入和非法聚类,实现了Web数据库安全索引。仿真实验表明,该算法能使数据库信息流在多层矢量自回归空间中充分展开,特征匹配度比传统算法显著提高,能有效排除非法数据输出,保证数据库安全索引。     

基于MVDR和ICA的语音识别方法研究

本文讨论了最小方差无失真响应建模方法,并与线性预测方法进行了比较,比较发现最小方差无失真响应滤波器能提供一个更好的原始语音包络。然后在研究ICA原理及FastICA快速算法的基础上,将MVDR参数提取方法与独立分量分析方法相结合,并与传统语音识别方法在有噪声和无噪声的情况下进行了比较,进而对识别率、计算时间等结果进行了分析。MVDR参数提取方法可以提高语音识别系统的识别率,但是会增加平均识别时间;而经过ICA特征变换后的语音识别系统具有较好的鲁棒性。     

Evolutionary minimization of the Rand index for speaker clustering

We propose an effective method for clustering unknown speech utterances based on their associated speakers. The method jointly optimizes the generated clusters and the required number of clusters by estimating and minimizing the Rand index. The metric reflects the clustering errors that arise when utterances from the same speaker are placed in different clusters; or when utterances from different speakers are placed in the same cluster. One useful characteristic of the Rand index is that its value only reaches the minimum when the number of clusters is equal to the size of the true speaker population. We approximate the Rand index by a function of the similarity measures between utterances and then use a genetic algorithm to determine the cluster in which each utterance should be located, such that the function is minimized. Our experiment results show that this novel speaker-clustering method outperforms conventional methods that use the Bayesian information criterion to determine the required number of clusters.     

Acoustic model adaptation using in-domain background models for dysarthric speech recognition

Speech production errors characteristic of dysarthria are chiefly responsible for the low accuracy of automatic speech recognition (ASR) when used by people diagnosed with it. A person with dysarthria produces speech in a rather reduced acoustic working space, causing typical measures of speech acoustics to have values in ranges very different from those characterizing unimpaired speech. It is unlikely then that models trained on unimpaired speech will be able to adjust to this mismatch when acted on by one of the currently well-studied adaptation algorithms (which make no attempt to address this extent of mismatch in population characteristics).In this work, we propose an interpolation-based technique for obtaining a prior acoustic model from one trained on unimpaired speech, before adapting it to the dysarthric talker. The method computes a ‘background’ model of the dysarthric talker's general speech characteristics and uses it to obtain a more suitable prior model for adaptation (compared to the speaker-independent model trained on unimpaired speech). The approach is tested with a corpus of dysarthric speech acquired by our research group, on speech of sixteen talkers with varying levels of dysarthria severity (as quantified by their intelligibility). This interpolation technique is tested in conjunction with the well-known maximum a posteriori (MAP) adaptation algorithm, and yields improvements of up to 8% absolute and up to 40% relative, over the standard MAP adapted baseline.     

A method for speech signal processing based on band filtering of the logarithmic spectrum

We propose a method for speech signal preprocessing based on band filtering of the logarithmic amplitude spectrum with a filter with odd impulse characteristic. With such filtering, we can detect local nonuniformities in the spectrum of a speech signal caused by abrupt inclinations of the vocal tract frequency characteristic, which represent useful features for speech recognition. We show examples of using the proposed approach on natural speech signals.     

新型车载语音识别系统中的一种关键技术

提出一种新型车载语音识别系统,采用帧能量与帧过零率的乘积作为指标量进行语音端点检测,以MFCC作为语音信号特征矢量,基于HMM语音识别模型进行语音识别。同时提出了一种新的抗噪语音识别方法,改进型重复Wiener滤波结合PUM模型进行抗噪语音识别,较好的抑制了噪声干扰,提高了语音识别率。     

相空间重构在语音情感识别中的研究

为了更为全面地表征语音情感状态,弥补线性情感特征参数在刻画不同情感类型上的不足,将相空间重构理论引入语音情感识别中来,通过分析不同情感状态下的混沌特征,提取Kolmogorov熵和关联维作为新的情感特征参数,并结合传统语音特征使用支持向量机（SVM）进行语音情感识别。实验结果表明,通过引入混沌参数,与传统物理特征进行识别的方案相比,准确率有了一定的提高,为语音情感的识别提供了一个新的研究途径。     

Harmonicity-Based Blind Dereverberation for Single-Channel Speech Signals

The distant acquisition of acoustic signals in an enclosed space often produces reverberant artifacts due to the room impulse response. Speech dereverberation is desirable in situations where the distant acquisition of acoustic signals is involved. These situations include hands-free speech recognition, teleconferencing, and meeting recording, to name a few. This paper proposes a processing method, named Harmonicity-based dEReverBeration (HERB), to reduce the amount of reverberation in the signal picked up by a single microphone. The method makes extensive use of harmonicity, a unique characteristic of speech, in the design of a dereverberation filter. In particular, harmonicity enhancement is proposed and demonstrated as an effective way of estimating a filter that approximates an inverse filter corresponding to the room impulse response. Two specific harmonicity enhancement techniques are presented and compared; one based on an average transfer function and the other on the minimization of a mean squared error function. Prototype HERB systems are implemented by introducing several techniques to improve the accuracy of dereverberation filter estimation, including time warping analysis. Experimental results show that the proposed methods can achieve high-quality speech dereverberation, when the reverberation time is between 0.1 and 1.0 s, in terms of reverberation energy decay curves and automatic speech recognition accuracy     

基于DSP技术的多路语音实时采集与压缩处理系统

介绍一个多路语音实时采集与压缩处理系统。该系统基于 PC- ISA总线结构 ,最大的特点是通过单片 DSP高性能价格比实时地实现了多达 10路的语音采集和 10路语音实时压缩及一路语音解压处理。该系统已成功应用于某语音记录设备中。     

一种改进的MFCC参数提取方法

为了提高语音识别率,提出了一种改进的MFCC参数提取方法。该方法应用小波包变换高分辨率的特点和语音高频加权的功能,在传统MFCC参数的基础上提取了一种新特征参数。新参数能对语音信号频率进行更加精细的划分,能够更稳定地减小频谱失真,且在一定程度上降低了信号的噪声。最后采用高斯混合模型（GMM）进行说话人语音识别,实验表明新特征参数取得了较好的识别率。     

A neural network model for speech intelligibility quantification

A neural network based model is developed to quantify speech intelligibility by blind-estimating speech transmission index, an objective rating index for speech intelligibility of transmission channels, from transmitted speech signals without resort to knowledge of original speech signals. It consists of a Hilbert transform processor for speech envelope detection, a Welch average periodogram algorithm for envelope spectrum estimation, a principal components analysis (PCA) network for speech feature extraction and a multi-layer back-propagation network for non-linear mapping and case generalisation. The developed model circumvents the use of artificial test signals by exploiting naturally occurring speech signals as probe stimuli, reduces measurement channels from two to one and hence facilitates in situ assessment of speech intelligibility. From a cognitive science viewpoint, the proposed method might be viewed as a successful paradigm of mimicking human perception of speech intelligibility using a hybrid model built around artificial neural networks.