Intel公司将要推出的新的系统技术

★增加的 AGP 支持,可用于语音识别和视频会议★Instantly Available PC 规范,可进一步加强电源管理和远程启动功能★Wired for Management 2.0规范★用于便携机的 AGP 技术     

一种基于STM32的智能家居控制系统

设计了以STM32单片机为核心的智能家居控制系统。该系统以语音识别技术和GSM通信技术为基础,完成控制台、手持设备及门禁密码锁之间的通信,实现对室内家居电器的远程控制。实验结果表明,该系统运行稳定,具有广泛的应用前景。     

基于语音识别和无线传感网络的智能家居系统设计

基于语音识别技术和无线传感网络,设计了一套能够通过远程语音遥控进行便捷控制的智能家居系统;分析了语音识别技术的基本原理和无线传感网络在智能家居中的应用,重点阐述了系统的设计方案以及主要硬件组成和软件设计思路;通过对特定人语音识别的训练和测试,该系统能够以较高的识别率和灵敏度完成对智能家电的远程控制;系统实时输出识别结果,识别速度快,抗干扰能力强,识别率可以达到98%,在语音远程控制领域具有广阔的市场前景和应用推广价值。     

基于AI+IOT的智慧家居系统

利用人工智能技术和深度学习算法,设计开发了基于AI+IOT的智慧家居系统。基于百度提供的免费的语音识别云平台,该系统使用ZigBee网络,对家居环境数据进行采集、分析,并通过物联网技术和人工智能技术实现远程语音控制各种家电的功能。基于深度学习,系统通过百度语音识别技术对自然语言进行语音识别,通过搭建系统编译环境成功融合了AI技术和IOT技术实现了具有语音控制功能的智能家居系统,致力于为人们提供更加便捷智能的生活。     

基于语音识别技术的智能家居主控设计

针对目前市场上大部分智能家居产品采用手机客户端控制,控制入口深、用户操作不便的问题,设计了一款具有语音交互功能的智能家居主控。该语音主控基于语音识别技术和Zig Bee技术,硬件上由科大讯飞AIUI语音处理模块、Zig Bee通信模块、麦克风阵列等组成。在软件上集成科大讯飞SDK编写了语音识别程序,实现家庭设备的语音控制;同时,编写软件程序实现手机客户端与语音主控的本地通信以及语音主控与阿里云服务器的远程通信;最后,设计实验并测试语音主控在本地与远程数据传输的延时与丢包情况。测试结果表明,本地与远程数据传输的平均延时分别为40ms和607ms,丢包率分别为0.038%和0.16%,数据传输的实时性与准确性可以满足智能家居场景的实际应用需求。     

让终端更便捷、更安全

随着科技的不断发展进步,各种终端设备的智能化和集成化程度越来越高,然而如何让我们从设备获取信息的方式越来越简单并且没有其他隐患,成为目前各种终端设备面临的问题。语音技术的应用,给人们带来了福音,它让这些问题都可以迎刃而解。语音技术包含语音合成和语音识别。语音合成将文本状态的信息转换成语音的形式输出。就像给设备安装上了嘴巴,你想让设备说什么,设备就可以说什么,解决了设备上的信息只能看不能听得问题。语音识别使得设备可以听懂用户的话,目前在嵌入式领域发展比较成熟的是命令词的识别。语音识别就像给设备安装了耳朵,让…     

语音识别的智能门控系统设计

该智能门卫系统采用语音识别技术.在陵阳单片机SPCE061A上实现.硬件部分主要由SPCE061A单片机、电源与门控电路、外扩存储器SPR4096、音频输入与输出电路等组成.主要技术是该单片机在语音识别中的应用和对门控电路的设计.该系统软件部分由语音训练模块、语音识别模块、语音数据处理模块、语音播报模块和密码输入与确认模块组成,通过对系统的初始化和识别训练实现了语音数据的采集、特征提取、特定语音识别和语音播放等功能.根据语音识别算法原理,对语音信号的预处理、特征提取、模式匹配做了分析.该系统实验结果表明,系统性能稳定,识别效果好,可用于家庭或小型办公场所的门锁控制.     

人脸识别技术在多媒体设备智能管理中的应用

提出了一种远程监控管理系统,该系统利用计算机通过网络监控异地多媒体设备的电源状态,并对设备使用者的信息进行管理。在系统中,采用了人脸识别技术作为用户身份认证手段,提高了系统的安全性。识别算法采用奇异值分解和BP神经网络相结合的方法,达到了实用效果。     

智能垃圾桶中的语音识别技术应用研究

阐述基于语音识别的智能垃圾桶的系统设计方案,将智能垃圾桶分为5大功能模块进行设计,此5大功能模块为红外线感测模块、语音识别及播报模块、机械控制模块、远程定位和通信模块,以及外观模块.要想在智能垃圾桶中应用语音识别技术,还需要设置各功能模块相对应的硬件和软件,通过硬件与软件的共同作用,实现智能垃圾桶各功能模块的正常运行,以此达成语音识别和语音宣导的作用.     

面向儿童的智能聊天机器人设计

文中设计可陪伴孩子聊天的机器人,以STM32F103ZET6单片机为控制中心,使用SYN7318语音识别模块、PAJ7620手势识别模块、OpenMV3 Cam M7摄像头模块、WiFi模块、HMI触摸屏和GSM模块等完成语音识别、合成,MP3播放,手势、图像获取及识别,远程语音通话。实验结果证明,所提系统能对语音进行准确的识别,并做出相应的动作;识别到关键词时开启远程亲子互动,家长可通过智能手机远程查看儿童的状态信息及相关图片。系统性能良好,操作简便,成本低廉,适合在智能化市场推广应用。     

基于嵌入式的语音识别系统

该文设计了基于嵌入式的语音识别系统。系统硬件需要实现与服务器连接生成二维码,对Led的驱动调用。系统软件则包括App应用的设计,科大讯飞语音识别技术的SDK移植、服务器的搭建以及TCP/IP协议与Socket通信机制进行网络通信交互的设计。App里的语音系统对A23中的Led具有高效的控制力,通过服务器的转发,Led能够对接收到的语音命令作出迅速的反应,从而进行相应的开灯或者关灯的操作。     

Efficient Noise Robust Feature Extraction Algorithms for Distributed Speech Recognition (DSR) Systems

The evolution of robust speech recognition systems that maintain a high level of recognition accuracy in difficult and dynamically-varying acoustical environments is becoming increasingly important as speech recognition technology becomes a more integral part of mobile applications. In distributed speech recognition (DSR) architecture the recogniser's front-end is located in the terminal and is connected over a data network to a remote back-end recognition server. The terminal performs the feature parameter extraction, or the front-end of the speech recognition system. These features are transmitted over a data channel to the remote back-end recogniser. DSR provides particular benefits for the applications of mobile devices such as improved recognition performance compared to using the voice channel and ubiquitous access from different networks with a guaranteed level of recognition performance. A feature extraction algorithm integrated into the DSR system is required to operate in real-time as well as with the lowest possible computational costs.In this paper, two innovative front-end processing techniques for noise robust speech recognition are presented and compared, time-domain based frame-attenuation (TD-FrAtt) and frequency-domain based frame-attenuation (FD-FrAtt). These techniques include different forms of frame-attenuation, improvement of spectral subtraction based on minimum statistics, as well as a mel-cepstrum feature extraction procedure. Tests are performed using the Slovenian SpeechDat II fixed telephone database and the Aurora 2 database together with the HTK speech recognition toolkit. The results obtained are especially encouraging for mobile DSR systems with limited sizes of available memory and processing power.     

Segment-based emotion recognition from continuous Mandarin Chinese speech

Recognition of emotion in speech has recently matured to one of the key disciplines in speech analysis serving next generation human-machine interaction and communication. However, compared to automatic speech recognition, that emotion recognition from an isolated word or a phrase is inappropriate for conversation. Because a complete emotional expression may stride across several sentences, and may fetch-up on any word in dialogue. In this paper, we present a segment-based emotion recognition approach to continuous Mandarin Chinese speech. In this proposed approach, the unit for recognition is not a phrase or a sentence but an emotional expression in dialogue. To that end, the following procedures are presented: First, we evaluate the performance of several classifiers in short sentence speech emotion recognition architectures. The results of the experiments show that the WD-KNN classifier achieves the best accuracy for the 5-class emotion recognition what among the five classification techniques. We then implemented a continuous Mandarin Chinese speech emotion recognition system with an emotion radar chart which is based on WD-KNN; this system can represent the intensity of each emotion component in speech. This proposed approach shows how emotions can be recognized by speech signals, and in turn how emotional states can be visualized.     

Harnessing graphics processors for the fast computation of acoustic likelihoods in speech recognition

In large vocabulary continuous speech recognition (LVCSR) the acoustic model computations often account for the largest processing overhead. Our weighted finite state transducer (WFST) based decoding engine can utilize a commodity graphics processing unit (GPU) to perform the acoustic computations to move this burden off the main processor. In this paper we describe our new GPU scheme that can achieve a very substantial improvement in recognition speed whilst incurring no reduction in recognition accuracy. We evaluate the GPU technique on a large vocabulary spontaneous speech recognition task using a set of acoustic models with varying complexity and the results consistently show by using the GPU it is possible to reduce the recognition time with largest improvements occurring in systems with large numbers of Gaussians. For the systems which achieve the best accuracy we obtained between 2.5 and 3 times speed-ups. The faster decoding times translate to reductions in space, power and hardware costs by only requiring standard hardware that is already widely installed.     

基于HTK 的特定词语音识别系统

语音识别技术经过半个世纪的发展,目前已日趋成熟,其在语音拨号系统、数字遥控、工业控制等领域都有了广泛的应用。由于目前常用的声学模型和语言模型的局限性,计算机只能识别一些词汇或一些句子。语音识别系统在语种改变时,往往会出现错误的识别结果。针对上述问题,结合隐马尔可夫模型原理,在HTK语音处理工具箱的基础上构建了中英文特定词语音识别系统。该系统通过代码控制整个构建过程,使其在更换新的训练数据和词典后能快速生成对应的识别模型。     

智能家庭语音遥控系统的设计与实现

语音识别技术是最近十年信息技术领域重要科技发展技术之一.现在的绝大多数家电都是用单独的遥控器,各自通过独立的微处理芯片进行控制.智能家庭语音识别系统是一种集成的嵌入式系统,它是一种非接触识别技术,通过用户的声音控制家电的开/关.     

基于SMS远程监控系统

SMS远程监控系统利用GSM网络的SMS(手机短信业务)和低成本的硬件电路构建家居设备的远程监控。在GSM覆盖的地域里,用户可以发送短消息来控制连接到系统的家电设备的开关状态。同样,系统也可以向用户告知其工作状态。用户可以设定特殊短信来监视家中是否发生了特定事件。将GSM/GPRS MODEM主控系统与无线模块相结合,系统构建了一个一主多从的控制网络,从而实现智能化家居监控网络。     

The use of speed-up techniques for a speech recognizer system

In speech recognition, not just the accuracy of an automatic speech recognition application is important, but also its speed. However, if we want to create a real-time speech recognizer, this requirement limits the time that is spent on searching for the best hypothesis, which can even affect the recognition accuracy. Thus the applied search method plays an important role in the speech recognition task, and so does its efficiency, i.e. how quickly it finds the uttered words. To speed up this search process, various ideas are available in the literature: we can use search heuristics, multi-pass search, or apply a family of aggregation operators. In this paper we test all these methods in turn, and combine them with a set of other novel speed-up ideas. The test results confirm that all of these techniques are valuable: using combinations of them helped make the speech recognition process over 12 times faster than the basic multi-stack decoding algorithm, and almost 11 times faster than the Viterbi beam search method.     

图书馆检索室微机远程管理

实现远程管理的方法很多,可以先做一个在远程计算机上运行的客户端程序,然后在本地计算机上做一个服务器程序,通过这两个程序直接的通讯,实现远程管理。本文介绍了应用使用System.Management基类实现关闭远程计算机。