改进的堆的枚举算法的研究

提出一种生成所有堆的枚举算法。该算法按照从深层次向低层次的顺序生成堆,采用单个数判断法。测试结果验证了该算法的有效性和可靠性。     

C#环境下的单片机控制的机器人的监控的通讯程序设计

单片机在机器人中有着广泛的应用,但是由于计算性能欠佳,无法完成复杂的功能,需要使用上位机对其进行监控,本文针对ZKRT-300机器人采用C#环境设计了监控程序,就通讯协议及数据处理进行了论述.     

最重要的是把负的变成正的

热闹喧嚣后的圣诞瘦街头,Reka Lorincz漫步在无人的巴塞罗那的一条小巷里,她独自伫立在橱窗前良久,端详着跳舞的圣诞老人,“为什么你只会在一年年末才来到我们的身边,然后销声匿迹一整年？那这一整年中,你都在哪里？”于是,在2004年,Reka把这些平日圣诞节过后被废弃的树、包装,甚至圣诞老人的头发,做成一系列作品。而她,也就这样走进了大众视野。     

基于软交换的SIP和MGCP的互通的研究

在软交换网络中,SIP和MGCP是两种非常重要的协议,然而,两者之间的互通也是个突出的问题.通过深入分析互通所涉及的协议转换、寻址、资源协商等关键问题的基础上,提出在媒体网关控制器实体中扩展互通功能模块的设计方案,该互通功能模块是用C语言编程实现,利用该互通功能模块建立起两个网络之间的点到点的呼叫.     

极富潮流时尚的外形的诺基亚的7260

全新的键盘设计令7260显得十分吸引人。这款银白红相间版更是把那种古典的优雅与现代的潮流完美的结合在一起．从美学的角度上来看诺基亚的7260可以说得上是一款设计相当精美的艺术品，具有相当高的观赏价值。     

适合的是最好的

中国有句老话:手巧不如家什妙。就是说,干哪行就得有哪行的专用工具,工具要是不好用,再巧的手干出的活恐怕也难随人意。     

适合的才是最好的

建立清晰合理的业务流程体系是ERP项目实施成功的关键因素，ERP作为一个管理工具主要提供决策支持数据及监控业务过程，而清晰合理的业务流程体系能保证ERP系统提供准确有效的数据而不是垃圾数据，同时保证业务系统更加规范，提高效率。     

强烈的对比 迷人的照片

需要软件:Adobe Phatashap C54以上版本你会学到:利用黑白图层和高反差保留滤镜加强结构需要时间:大约25分钟相机拍摄下来的东西,有的细节眼睛是看不到的。像海水的纹理或者岸边岩石的表面这样一些具有低对比度的细致图案,在乌云下会显得更不明显。不过通过图像编辑,更准确地说是通过效的提高对比度,可以让这些结构更明显可见,并因此更好地展现照片的魅力。最简单的办法是通过滑块、色阶或者曲线加强对比度。不过,如果过度编辑     

游戏的躯壳 电影的灵魂

最近,视频网站土豆网和中影集团联合主办的＂2010土豆映像节＂揭晓赛果：创造网络点击纪录的《网瘾战争》从5585个参赛作品中脱颖而出,荣膺份量最重的金土豆奖（即最佳短片奖）。这是一幅疯狂的胜景：64分钟的动画影片,中国制造,0元成本;影片迅速占领各大视频网站头条;网络论坛上,斑竹不得不设立集中讨论帖。以至于尽管映像节评委会成员杨庆和宁财神都表示并不欣赏该片,但评委会最终还是将份量最重的＂金土豆奖＂颁给了它,以显示对网民意见的尊重。     

用户的希望就是我们的希望 合作伙伴的成功就是我们的成功

就全球自动化控制技术领导者霍尼韦尔(Honeywell)新上市单户型智能家居系统,访其安防业务亚太区研发总监贾琢成贾琢成先生于2005年1月加入霍尼韦尔安防事业集团,出任亚太区研发总监。他领导整合了亚太区各国的技术资源,在韩国首尔设立了全球视频技术研发中心和智能家居研发中心,创建了霍尼韦尔安防上海研发中心和深圳研发中心,目前上海安防研发中心已经成为霍尼韦尔安防在全球最大的研发中心。贾琢成先生拥有中欧工商学院EMBA学位。他凭借丰富的专业经验和出色的领导才能带领霍尼韦尔安防业务亚太地区研发团队不断进取,创造佳绩。     

结合关键词混淆网络的关键词检出系统

为了高效地从大词汇量连续语音识别（LVCSR）的多候选中得到关键词结果,保证最小词错误率,提出了将混淆网络的思想应用到关键词检出系统中.在传统混淆网络生成方法基础上,提出一种改进的更加适合于关键词检出的关键词混淆网络作为关键词检出的中间结构,该方法只对所有关键词竞争候选生成带有得分标记的关键词混淆网络,突出候选之间竞争关系,并根据得分标记确定关键词.与传统的N best作为中间结构的关键词检出系统比较,基于混淆网络的关键词检出系统的召回率为87.11％,提高了21.65%.实验表明,在提高召回率的同时,所提方法具有关键词直接定位的特点,因此具有较低的时间开销.     

基于关键词识别的语音到手势的转换

针对言语障碍者与正常人的交流问题,提出了一种利用关键词识别技术实现语音到手势转换的方法。首先,对采集到的语音信号,运用关键词识别技术识别出关键词。同时,根据《中国手语》,采用三维建模技术建立关键词对应的三维手势模型。最后,利用Open GL播放识别出的关键词对应的三维手势模型,从而实现了语音到手势的转换。实验结果表明,字母和数字的语音关键词的平均识别率达到90.1%,转换后的手势平均MOS(Mean Opinion Score)得分为4.4分,能够应用于正常人与言语障碍者的交流。     

Character confidence based on N-best list for keyword spotting in online Chinese handwritten documents

In keyword spotting from handwritten documents by text query, the word similarity is usually computed by combining character similarities, which are desired to approximate the logarithm of the character probabilities. In this paper, we propose to directly estimate the posterior probability (also called confidence) of candidate characters based on the N-best paths from the candidate segmentation-recognition lattice. On evaluating the candidate segmentation-recognition paths by combining multiple contexts, the scores of the N-best paths are transformed to posterior probabilities using soft-max. The parameter of soft-max (confidence parameter) is estimated from the character confusion network, which is constructed by aligning different paths using a string matching algorithm. The posterior probability of a candidate character is the summation of the probabilities of the paths that pass through the candidate character. We compare the proposed posterior probability estimation method with some reference methods including the word confidence measure and the text line recognition method. Experimental results of keyword spotting on a large database CASIA-OLHWDB of unconstrained online Chinese handwriting demonstrate the effectiveness of the proposed method.     

Point Process Models for Spotting Keywords in Continuous Speech

We investigate the hypothesis that the linguistic content underlying human speech may be coded in the pattern of timings of various acoustic “events” (landmarks) in the speech signal. This hypothesis is supported by several strands of research in the fields of linguistics, speech perception, and neuroscience. In this paper, we put these scientific motivations to the test by formulating a point process-based computational framework for the task of spotting keywords in continuous speech. We find that even with a noisy and extremely sparse phonetic landmark-based point process representation, keywords can be spotted with accuracy levels comparable to recently studied hidden Markov model-based keyword spotting systems. We show that the performance of our keyword spotting system in the high-precision regime is better predicted by the median duration of the keyword rather than simply the number of its constituent syllables or phonemes. When we are confronted with very few (in the extreme case, zero) examples of the keyword in question, we find that constructing a keyword detector from its component syllable detectors provides a viable approach.      

Keyword detection in conversational speech utterances using hidden Markov model based continuous speech recognition

This paper describes a set of modeling techniques for detecting a small vocabulary of keywords in running conversational speech. The techniques are applied in the context of a hidden Markov model (HMM) based continuous speech recognition (CSR) approach to keyword spotting. The word spotting task is derived from the Switchboard conversational speech corpus, and involves unconstrained conversational speech utterances spoken over the public switched telephone network. The utterances in this task contain many of the artifacts that are characteristic of unconstrained speech as it appears in many telecommunications based automatic speech recognition (ASR) applications. Results are presented for an experimental study that was performed on this task. Performance was measured by computing the percentage correct keyword detection over a range of false alarm rates evaluated over 2·2 h of speech for a 20 keyword vocabulary. The results of the study demonstrate the importance of several techniques. These techniques include the use of decision tree based allophone clustering for defining acoustic subword units, different representations for non-vocabulary words appearing in the input utterance, and the definition of simple language models for keyword detection. Decision tree based allophone clustering resulted in a significant increase in keyword detection performance over that obtained using tri-phone based subword units while at the same time reducing the size of the inventory of subword acoustic models by 40%. More complex representations of non-vocabulary speech were also found to significantly improve keyword detection performance; however, these representations also resulted in a significant increase in computational complexity.     

Keyword spotting using an evolutionary-based classifier and discriminative features

Keyword spotting refers to detection of all occurrences of any given keyword in input speech utterances. In this paper, we define a keyword spotter as a binary classifier that separates a class of sentences containing a target keyword from a class of sentences which do not include the target keyword. In order to discriminate the mentioned classes, an efficient classification method and a suitable feature set are to be studied. For the classification method, we propose an evolutionary algorithm to train the separating hyper-plane between the two classes. As our discriminative feature set, we propose two confidence measure functions. The first confidence measure function computes the possibility of phonemes presence in the speech frames, and the second one determines the duration of each phoneme. We define these functions based on the acoustic, spectral and statistical features of speech. The results on TIMIT indicate that the proposed evolutionary-based discriminative keyword spotter has lower computational complexity and higher speed in both test and train phases, in comparison to the SVM-based discriminative keyword spotter. Additionally, the proposed system is robust in noisy conditions.     

基于特征空间轨迹匹配方式的语音关键词检测法

语音关键词识别是近年来颇受重视的一个研究领域,文章基于特征空间轨迹的时间规整化原理,提出了一种高性能的关键词检测法,并探讨了轨迹等分长度对该算法检测性能的影响。实验结果表明,基于特征空间轨迹匹配方式的关键词检测法的检测性能接近于人工检测,具有一定的实用性。     

Improving the performance of keyword spotting system for children's speech through prosody modification

Searching for words of interest from a speech sequence is referred to as keyword spotting (KWS). A myriad of techniques have been proposed over the years for effectively spotting keywords from adults' speech. However, not much work has been reported on KWS for children's speech. The speech data for adult and child speakers differs significantly due to physiological differences between the two groups of speakers. Consequently, the performance of a KWS system trained on adults' speech degrades severely when used by children due to the acoustic mismatch. In this paper, we present our efforts towards improving the performance of keyword spotting systems for children's speech under limited data scenario. In this regard, we have explored prosody modification in order to reduce the acoustic mismatch resulting from the differences in pitch and speaking-rate. The prosody modification technique explored in this paper is the one based on glottal closure instant (GCI) events. The approach based on zero-frequency filtering (ZFF) is used to compute the GCI locations. Further, we have presented two different ways for effectively applying prosody modification. In the first case, prosody modification is applied to the children's speech test set prior to the decoding step in order to improve the recognition performance. Alternatively, we have also applied prosody modification to the training data from adult speakers. The original as well as the prosody modified adults' speech data are then augmented together before learning the statistical parameters of the KWS system. The experimental evaluations presented in this paper show that, significantly improved performances for children's speech are obtained by both of the aforementioned approaches of applying prosody modification. Prosody-modification-based data augmentation helps in improving the performance with respect to adults' speech as well.     

一种新的基于分层查询表的关键词识别模型

关键词识别是近年来语音识别研究的一个热点。提出了一种新的基于分层查询表的关键词识别模型,该模型具有简单、实用、快速的特点。利用该模型实现了路况信息查询系统,取得了较高的识别率,具有一定的实用性。