人机语音智能对话系统在英语实验室中的应用研究

当前,主流的英语学习环境主要集中在学校的英语实验室中,而这样统一化的标准学习环境很难适应不同层次学生的英语学习需求。因此,为了进一步提升学生学习英语的积极性与适应性,提出以兴趣推荐为导向的改进型人机语音智能英语对话系统。该系统以基础服务层为英语语音采集层,将数据核心识别与处理模块作为信息编辑运营层,利用消息总线作为接入层通过Web或移动终端作为交互层,通过协同过滤算法将学生的兴趣指标作为核心处理数据,进而对不同的学生个性化地推荐其感兴趣的对话知识,从而促进英语实验室中学生进行个性化交互学习的积极性。实验仿真验证表明,所提出的人机语音智能对话系统能够提升学生英语学习个性化兴趣推荐的效果。     

基于智能语音识别的机器人研究应用

基于智能语音识别的机器人能够利用机器人辅助学习音乐,提高学习效率和学习兴趣。本智能语音识别机器人利用人工智能技术与用户进行语音交流,识别用户的指令和需求,机器人配合做相应的动作。     

利用智能语音处理器实现嘈杂环境下的语音识别

通过语音方式输入信息或进行设备控制不仅解放使用者双手,且操作更为方便快捷。然而嘈杂的使用环境常常会对设备语音识别造成干扰形成误判,智能语音处理器或将可以解决这一难题。智能移动终端设备自问世以来,厂商们就不断在人机交互方式上进行创新,从最早期的按键控制发展到目前的触摸控制,随着以苹果Siri为代表的语音控制应用的流行,语音交互逐渐开始得到广泛应用。语音交互可以解放使用者的双手,快捷方便地输入信息或者对     

移动信息系统中的PKI应用

移动通信系统不仅能够提供传统的语音业务,而且能够提供如短信、上网等数据信息服务;移动设备因具有便携性的特点,成为了移动办公、移动电子商务、移动行业信息系统等应用的首选终端.但由于移动网络为无线网络,因此,电子商务以及某些特殊行业的移动信息系统等应用对安全性要求更高,为此,提出了通过公钥基础设施(PKI)来满足移动电子商务等移动信息系统的安全性要求.     

基于Android平台的智能英语发音训练系统的设计与研究

随着计算机科学技术的发展,英语学习软件的研发和应用数量也逐渐增多.在英语的学习中,智能英语发音训练是练习英语口语的重要部分,目前在英语的发音训练研究中语音识别技术受到高度的关注.随着移动互联网技术的发展,基于Android平台的便携移动设备作为安装英语发音辅助学习系统的主要工具得到了广泛的应用.本文对Android应用程序和英语教学中的英语发音训练进行了分析和研究,在Android平台的基础之上提出了智能英语发音训练系统设计的方案.     

智能家居控制装置的设计与实现

针对现有智能家居控制系统存在人机交互不便、便携性与兼容性比较差等缺点,设计一种以STM32微控制器为控制核心的智能家居控制装置。该装置采用主机与执行模块分离,通过无线WiFi进行通信;执行模块利用红外线与继电器实现对家居的控制;主机采集家电的遥控器发出的红外线波形,再把原样波形的数据传输给执行模块来控制家电设备;采用PWM等效电压控制方法来调整PWM信号占空比,实现光立方的立体动态显示;使用语音识别与光立方模块进行立体动态显示和语音反馈。经实验测试表明,该装置实现了对智能家居的有效控制,具有较好的兼容性与便携性,克服了现有装置的缺点。     

在线连续交互式英语语音智能识别系统设计

英语语音智能识别系统的发展方向是在线、连续性的人机交互,为此,设计在线连续交互式英语语音智能识别系统,并基于梅尔频率倒谱系数设计语音识别函数。系统的参考数据库选取NOSE算法计算语音评价分数并查错,利用专家知识库纠正用户英语语音。系统由语音训练模块和在线连续交互式识别模块组成,前者为系统数据库提供英语语音的语料、音标资源,后者实施系统数据库的评价、纠正工作。实验结果证明设计的系统语音识别率高且识别时间短,有效性强。     

英语语音优化识别建模仿真分析

随着中国经济高速发展以及全球一体化的进程,英语成为了人们日常交流必不可少的工具,然而对于初学者来说,能够通过语音识别技术将语音信号转化成文本的格式,更有利于快速掌握英语。而且语音识别技术经过多年的发展依然具有巨大的挖掘潜力,面对移动互联网的快速发展,通过对实时通信工具的大数据量的需求爆发,英语语音识别的实时性和系统稳定性越来越受到关注,文中分析了常用的传统语音识别技术,例如动态时间规整、神经网络模型和隐马尔可夫模型等,运用隐马尔可夫模型对语音信号进行处理和识别,提取出特征参数,与经过训练的模型体系进行匹配,找出最优的识别序列。然后在PC平台上,利用MATLAB建模仿真,基本实现了英语语音短句的识别,对于后续的硬件产品实现打下了良好的基础,具有积极的现实意义。     

基于智能移动设备的A超眼球生物测量系统

眼球生物测量具有重要的临床意义,为了提高眼球生物测量设备的便携性与经济性,文中提出一种基于智能移动设备的A超眼球生物测量系统。该系统由数据采集模块和智能移动设备构成,两者通过蓝牙接口进行信息交互,数据采集模块用于发射超声和接收回波,智能移动设备用于显示和处理回波数据、整机控制及远程通信。体模实验和在体实验均表明,文中系统测量结果准确可靠,稳定性好。如果医师利用自带的智能移动设备,可以大大降低眼球生物测量的成本值得在临床推广。     

讯宝推出新的语音识别移动计算解决方案

美国讯宝科技公司宣布推出支持语音识别功能的新版MC9000系列移动计算机M C9060-K和MC9060-S,这两款计算机是为那些要求免持语音识别和语音提示操作应用的仓库和分销客户专门设计,目的在于提高企业移动系统的工作效率和实时数据捕捉能力。支持语音识别功能的MC9060将讯宝的耐用硬件与合作伙伴的软件和附件有机结合在一起,来提高包括分销中心订单拣选、收货、补货、装货、维护、修理、包装分类和搬运、退货处理和检查等行业应用的工作效率。MC9060全新语音识别解决方案可为客户提供一种多功能的设备,以支持包括语音识别、条形码扫描、成…     

Speech and Language Processing for Multimodal Human-Computer Interaction

In this paper, we describe our recent work at Microsoft Research, in the project codenamed Dr. Who, aimed at the development of enabling technologies for speech-centric multimodal human-computer interaction. In particular, we present in detail MiPad as the first Dr. Who's application that addresses specifically the mobile user interaction scenario. MiPad is a wireless mobile PDA prototype that enables users to accomplish many common tasks using a multimodal spoken language interface and wireless-data technologies. It fully integrates continuous speech recognition and spoken language understanding, and provides a novel solution to the current prevailing problem of pecking with tiny styluses or typing on minuscule keyboards in today's PDAs or smart phones. Despite its current incomplete implementation, we have observed that speech and pen have the potential to significantly improve user experience in our user study reported in this paper. We describe in this system-oriented paper the main components of MiPad, with a focus on the robust speech processing and spoken language understanding aspects. The detailed MiPad components discussed include: distributed speech recognition considerations for the speech processing algorithm design; a stereo-based speech feature enhancement algorithm used for noise-robust front-end speech processing; Aurora2 evaluation results for this front-end processing; speech feature compression (source coding) and error protection (channel coding) for distributed speech recognition in MiPad; HMM-based acoustic modeling for continuous speech recognition decoding; a unified language model integrating context-free grammar and N-gram model for the speech decoding; schema-based knowledge representation for the MiPad's personal information management task; a unified statistical framework that integrates speech recognition, spoken language understanding and dialogue management; the robust natural language parser used in MiPad to process the speech recognizer's output; a machine-aided grammar learning and development used for spoken language understanding for the MiPad task; Tap & Talk multimodal interaction and user interface design; back channel communication and MiPad's error repair strategy; and finally, user study results that demonstrate the superior throughput achieved by the Tap & Talk multimodal interaction over the existing pen-only PDA interface. These user study results highlight the crucial role played by speech in enhancing the overall user experience in MiPad-like human-computer interaction devices.     

Designing for learnability in human-robot communication

In a future scenario where many devices can be controlled using the voice, easy and intuitive access will be crucial for avoiding cognitive overload when users are faced with many different systems and interaction models. We propose a model for interaction with spoken language interfaces applied to heterogeneous tasks for service robots, based on the idea of using a family of lifelike characters. We argue that we can signal important features of the speech interface by using certain visual cues. The aim is to facilitate learning and transfer between interfaces. We discuss challenges for dialogue design affecting learnability in the light of the speech interface constructed for our full-scale robot prototype CERO.     

基于语境感知和迁移学习的口语理解方法

口语理解是语言处理中的重要组成部分.在语言翻译中,口语理解可以将自然语言用户的话语解析为语义框架.为了提高口语理解的性能,在多回合对话中可以加入上下文信息.但是收集大规模的人工标记的多轮对话语料库是复杂,昂贵的,且不现实的.所以在此提出了一个上下文编码语言转换器(CLT)模型,以便于利用各种上下文信息.实验结果表明,在...     

A survey on deploying mobile deep learning applications: A systemic and technical perspective

With the rapid development of mobile devices and deep learning, mobile smart applications using deep learning technology have sprung up. It satisfies multiple needs of users, network operators and service providers, and rapidly becomes a main research focus. In recent years, deep learning has achieved tremendous success in image processing, natural language processing, language analysis and other research fields. Despite the task performance has been greatly improved, the resources required to run these models have increased significantly. This poses a major challenge for deploying such applications on resource-restricted mobile devices. Mobile intelligence needs faster mobile processors, more storage space, smaller but more accurate models, and even the assistance of other network nodes. To help the readers establish a global concept of the entire research direction concisely, we classify the latest works in this field into two categories, which are local optimization on mobile devices and distributed optimization based on the computational position of machine learning tasks. We also list a few typical scenarios to make readers realize the importance and indispensability of mobile deep learning applications. Finally, we conjecture what the future may hold for deploying deep learning applications on mobile devices research, which may help to stimulate new ideas.     

云计算环境下的外语移动学习智慧平台研究

随着云计算技术在智慧校园教育教学中的应用,在高校的外语教育教学中也把云计算引入了进来,基于云计算的外语移动学习智慧平台不仅提高了学生外语学习水平,而且外语移动学习智慧平台改变了传统的外语教育教学方法,提高了学生对外语学习的能力.本文对云计算技术的概念及应用进行了分析,对外语移动学习的特点和意义进行了研究,提出了基于云计算的外语移动学习智慧平台体系结构建设,并对基于云计算的环境下外语移动学习智慧平台建设进行了分析和研究.     

Language,modality, and mobile media use experiences: Social responses to smartphone cues in a task-oriented context

With people’s growing use of virtual agents and voice assistants on smartphones, researchers have pointed out that mobile phones are not only acting as intermediaries that connect users from different places, but also communication terminals that present different combinations of social cues. This study applies the Computers are Social Actors paradigm in human-phone interaction and postulates that compared to non-anthropomorphic language and text cues, anthropomorphic language and vocal cues will have more effects on users’ social responses to smartphones. This study also explores the role of individual differences in users’ social responses to smartphones. Based on a lab experiment using a between-subjects factorial design, the study suggests that although anthropomorphic language and voice-based information did not have main effects on users’ social responses, people’s mobile media usage and intensive phone use interacted with these cues in their social responses to the smartphones. In addition, this study implies that it is the combination of social cues, individual differences, and communication contexts that contributes to people’s social reactions to the smartphones. The findings of the study can inform user interface design and precipitate further discussion about the ethical issues in human-phone interaction.     

一种改进的深度神经网络应用于推荐系统

推荐系统是信息过滤的一种重要工具。随着互联网和大数据的介入,推荐系统的技术革新面临着新的挑战。近年来,深度学习的革命性进步在语音识别、图像分析和自然语言处理方面都受到了广泛关注。与此同时,一种应用于许多复杂任务的最先进的机器学习技术被用于推荐系统,以提高推荐的质量。由于其一流的性能表现和高质量的推荐结果,深度学习可以更好地理解用户需求、项目特征及其之间的历史性互动。文章提出将一种改进的深度神经网络应用于推荐系统。实验结果表明,该方法的效果令人瞩目。     

A Time and Location Information Assisted OTP Scheme

As the mobile networks are springing up, mobile devices become a must gadget in our daily life. People can easily access Internet application services anytime and anywhere via the hand-carried mobile devices. Most of modern mobile devices are equipped with a GPS module, which can help get the real-time location of the mobile device. In this paper, we propose a novel authentication scheme which exploits volatile passwords—One-Time Passwords (OTPs) based on the time and location information of the mobile device to transparently and securely authenticate users while accessing Internet services, such as online banking services and e-commerce transactions. Compared to a permanent password base scheme, an OTP based one can prevent users from being eavesdropped. In addition to a memoryless feature, the scheme restricts the validness of the OTP password not only in a certain time period but also in a tolerant geometric region to increase the security protection. However, if a legitimate user is not in the anticipated tolerant region, the user may fail to be authenticated. Hence, a Short Message Service based mutual authentication mechanism is also proposed in the article to supplement the unexpected misjudgement. The proposed method with a volatile time/location-based password features more secure and more convenient for user authentication.