基于语音识别的自动标绘系统设计与实现

介绍了自动标绘系统的需求和系统流程，并对语音采集、语音识别和自动标绘3个阶段的技术难点及解决方案进行了详细介绍。重点介绍了采用语音自动重叠技术以减少语音切割失误，提高识别精度。描述了对识别出的数字串运用打分系统进行智能切分，提取数据点以提高标绘精度。系统采用VC．NET和SQL Server实现，稳定环境下语音识别精度达到97％以上，标绘正确率达到95％，较好地实现了系统需求。     

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。     

局域网并行处理在语音识别中的应用

在语音识别中，不论是训练语音识别系统，还是使用系统识别语音，都需要进行大量的数据处理，这使得语音识别的研究和实现都变的非常困难。本文提出一种基于局域网的分布式计算机系统的快速并行数据处理方法来实现语音识别的模型训练和语音的识别，不仅加 了训练和识别的速度，节约了大量的时间，而且降低了语音识别任务对硬件的要求，取得了满意的效果。     

情感语音识别技术在军用智能机器人中的应用

提出了一种基于隐马尔可夫模型(HMM)与人工神经网络(ANN)相结合的情感语音识别系统的实现方法.并从情感语音资料的获取、情感语音特征的提取及情感语音识别等方面阐明了情感语音识别系统的建立过程.该系统实现了情感语音特征参数的提取、情感语音模型参数的训练及对录入的情感语音进行识别等功能.研究结果表明了该系统识别效果良好.     

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.     

基于国产蓝牙芯片FR8016HA的语音识别系统

本文介绍了语音识别系统芯片国产替代方案的实现.语音识别系统的硬件核心芯片采用富芮坤FR8016HA,系统接收由移动端通过蓝牙发送来的音频信号,之后对语音信号进行识别,最终将识别结果呈现到LCD显示屏上,实现人机交互.语音算法采用改进的DTW模板匹配法,通过测试,本系统语音识别正确率可达82％.     

混合连接时间/注意力机制端到端语音识别

为提高常规自动语音识别(ASR)系统的精度,提出基于隐式马尔可夫模型混合连接时间分类/注意力机制的端到端ASR系统设计方法。首先,针对可观测时变序列语音识别过程中存在的连续性强、词汇量大的语音识别难点,基于隐式马尔可夫模型对语音识别过程进行模拟,实现了语音识别模型参数化;其次,使用连接时间分类目标函数作为辅助任务,在多目标学习框架中训练语音识别过程的关注模型编码器,可降低序列级连接时间分类目标近似度,实现语音识别过程精度提升;最后,通过在自建语音识别库上的仿真实验,验证所提算法在识别效率和精度上的性能优势。     

中等词汇的汉英语音翻译系统

本文给出汉英语音翻译系统的组成，介绍了系统中连续汉语语音识别和汉英机器翻译的工作；我们已经在限定主题、中等词汇量的条件下实现了非特定人的连续语音识别，实现了汉英语音翻译实验演示系统。     

基于Java Speech API规范的语音识别引擎的实现

本文介绍了Java Speech API(JSAPI)规范的语音识别引擎的系统框架，描述了采用已有的C／C 识别引擎实现JSAPI语音识别引擎的思路和实现策略，提出并分析了以事件处理和状态处理为核心来实现JSAPI规范的具体方法，完成了基于JSAPI规范的语音识别软件系统的实现。     

沪语语音识别研究及在家居机器人中的应用

为实现对沪语语音的识别和与家居机器人沪语语音交互,通过分析了沪语语言的语音、语调、语法特点,提出了沪语语音的识别基元的建模方法.该方法生成了新的声韵集作为识别基元,并建立了课题相关的沪语语音语料库,同时基于HTK初步构造了沪语语音的声学模型和3-Gramm语言模型.该系统模型在家居服务机器人中得到初步的应用,系统采用V...     

基于Kaldi的语音识别

人工智能技术是当前计算机科学的研究热点,人机通信是人工智能技术的重要组成之一。作为人机通信主要方法之一的语音交互也一直是科学家的研究热点,语音交互技术的关键是语音识别。而目前大多语音识别软件要么功能单一,要么价格昂贵,Kaldi作为新兴的开源语音识别工具,凭借其强大的功能和简单的获取渠道逐渐流行。该文介绍了语音识别技术的发展历程,Kadli软件的基本架构和其所具有的独特优势,语音识别的一般处理流程,多层神经网络的基本结构以及多层神经网络在语音识别当中的应用。对基于Kaldi软件当中的HMM-DNN模型,使用中文数据集训练该模型,搭建一个完整的语音识别系统。通过该系统,不仅能展现出Kaldi软件丰富强大的功能,同时也为语音识别研究人员选择合适的工具提供了新的思路。     

基于UniSPeech-SDA80D51的车载音响声控系统

提出一种语音命令控制车载音响操作的设计方案,以德国Infineon公司新推出的具有DSP和单片机双核的SoC语音处理芯片UniSpeech-SDA80D51为核心组成非特定人车载音响语音控制系统,并实现了系统样机的研制.该系统在江淮同悦SL1102C1型车载音响上进行了语音控制实测,实测数据表明系统语音识别率可达到95...     

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.     

智能机器狗的语音控制模型研究

阐述语音人机交互手段的必要性及意义,提出一种基于语音控制的机器狗系统解决方案。该系统利用HTK和Julius构建大词汇量连续的语音识别平台,采用语音识别技术提取语音命令用于机器狗控制,并结合有限状态机的控制理论设计机器狗控制系统模型。测试结果表明,该系统能较好地识别用户语音命令,控制系统模型能有效完成机器狗在不同状态下的控制任务。     

智能语音情感分析系统的设计与实现

为了将语音情感识别方法投入实际应用,设计了一种智能语音情感分析系统。该系统基于分布式架构,实现了大量语音信号的输入与存储,结合流媒体技术,实现了情感的实时分析,历史录音的访问与调取;内部人工智能分析算法达到了较高的识别准确率,并能自动识别消极情感并报警,对检测对象的情感状态进行了良好的检测。     

Automatic speech recognition- an approach for designing inclusive games

Computer games are now a part of our modern culture. However, certain categories of people are excluded from this form of entertainment and social interaction because they are unable to use the interface of the games. The reason for this can be deficits in motor control, vision or hearing. By using automatic speech recognition systems (ASR), voice driven commands can be used to control the game, which can thus open up the possibility for people with motor system difficulty to be included in game communities. This paper aims at find a standard way of using voice commands in games which uses a speech recognition system in the backend, and that can be universally applied for designing inclusive games. Present speech recognition systems however, do not support emotions, attitudes, tones etc. This is a drawback because such expressions can be vital for gaming. Taking multiple types of existing genres of games into account and analyzing their voice command requirements, a general ASRS module is proposed which can work as a common platform for designing inclusive games. A fuzzy logic controller proposed then is to enhance the system. The standard voice driven module can be based on algorithm or fuzzy controller which can be used to design software plug-ins or can be included in microchip. It then can be integrated with the game engines; creating the possibility of voice driven universal access for controlling games.     

基于实例推理的人机对话系统的设计与实现

为了给英语学习者建立一个虚拟的环境，使其通过与机器进行对话练习，达到学习的目的，本文采用基于实例推理的方法，结合人机对话、语音识别和语音合成技术，研究了一个辅助英语学习的人机对话系统的设计与实现。文章重点阐述了系统的语音功能、对话管理和实例库访问。实验表明，系统很好地满足了用户提高英语听力和口语水平的需求。     

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。     

基于连续语音识别算法和词树约束的汉语词组语音识别

具有确定词表的词组语音识别是语音识别研究的一个重要方面,应用相当广泛。本文在简单介绍词组语音识别之后给出了一个基于连续语音识别算法和词树约束的汉语词组语音识别方法。这种方法通过在束搜索连续语音识别算法中引入词树约束信息,发挥了连续语音识别算法的优点,并且充分利用了确定词表的约束信息,提高了计算和搜索的效率。然后介绍了约束词树和它的高效存储结构,这种结构提高了约束词树的存储效率和在识别搜索中的检索效率;最后给出实验的结果和讨论并进行简要的总结。     

Speech Recognition and In-Vehicle Telematics Devices: Potential Reductions in Driver Distraction

Speech Recognition is frequently cited as a potential remedy to distraction resulting from drivers' operation of in-vehicle devices. This position typically assumes that the introduction of speech recognition will result in reduced cognitive workload and improved driving performance. Past research neither fully supports nor fully discounts this assumption. However, it is difficult to compare many of these studies, due to differences in device operation tasks, the pacing of those tasks, speech recognition system performance, and system interface designs. In an effort to directly address the effect of voice recognition on driver distraction, the present authors developed a capability to manipulate the performance characteristics of a speech recognition system through a Wizard of Oz speech recognition system and installed this system in a simulated driving environment. The sensitivity of the simulated driving environment and speech recognition accuracy manipulation were evaluated in an initial study comparing driver cognitive workload and driving performance during self-paced simulated operation of a personal digital assistant (PDA) during no PDA use, manual control of the PDA, and speech control of the PDA. In the Speech PDA condition, speech recognition accuracy was varied between drivers. Analysis of drivers' emergency braking response times and rated cognitive workload revealed significantly lower cognitive demand and better performance in the No PDA condition when compared to the Manual PDA condition. The Speech PDA condition resulted in response times and rated cognitive workload levels that were between the No PDA and Manual PDA conditions, but not significantly different from either of these conditions. Further analysis of emergency braking performance revealed a non-significant trend towards better performance in conjunction with higher speech recognition accuracy levels. The potential for reducing driver distraction through the careful development and evaluation of speech recognition systems is discussed.