CELP算法软件实现

介绍了CELP算法原理,讨论了算法软件实现的有关问题,并给出在PC机上的实现结果.     

Trends in Agent Communication Language

Agent technology is an exciting and important new way to create complex software systems. Agents blend many of the traditional properties of AI programs—knowledge–level reasoning, flexibility, proactiveness, goal–directedness, and so forth—with insights gained from distributed software engineering, machine learning, negotiation and teamwork theory, and the social sciences. An important part of the agent approach is the principle that agents (like humans) can function more effectively in groups that are characterized by cooperation and division of labor. Agent programs are designed to autonomously collaborate with each other in order to satisfy both their internal goals and the shared external demands generated by virtue of their participation in agent societies. This type of collaboration depends on a sophisticated system of inter–agent communication. The assumption that inter–agent communication is best handled through the explicit use of an agent communication language (ACL) underlies each of the articles in this special issue. In this introductory article, we will supply a brief background and introduction to the main topics in agent communication.     

To Commit or Not to Commit: Modeling Agent Conversations for Action

Conversations are sequences of messages exchanged among interacting agents. For conversations to be meaningful, agents ought to follow commonly known specifications limiting the types of messages that can be exchanged at any point in the conversation. These specifications are usually implemented using conversation policies (which are rules of inference) or conversation protocols (which are predefined conversation templates). In this article we present a semantic model for specifying conversations using conversation policies. This model is based on the principles that the negotiation and uptake of shared social commitments entail the adoption of obligations to action, which indicate the actions that agents have agreed to perform. In the same way, obligations are retracted based on the negotiation to discharge their corresponding shared social commitments. Based on these principles, conversations are specified as interaction specifications that model the ideal sequencing of agent participations negotiating the execution of actions in a joint activity. These specifications not only specify the adoption and discharge of shared commitments and obligations during an activity, but also indicate the commitments and obligations that are required (as preconditions) or that outlive a joint activity (as postconditions). We model the Contract Net Protocol as an example of the specification of conversations in a joint activity.     

电脑语音识别和处理技术

本文介绍了COMPAQ DESKPRO/i及以上微型机的事务语音系统,同时还分析电脑语音识别和处理技术的概念、原理和构成。     

论伯恩斯的借代修辞格

在任何一种语言中,诗人和文学作者总趋于采用修辞手段或修辞格,以达到更直接、更有效、更生动、更感人地表达他们的思想之目的.本文就劳动人民自己的著名的苏格兰诗人伯恩斯在他诗歌中所惯于采用的修辞格进行了讨论;并就他所使用的借代(metonymy)手段与汉语和俄语中的借代手段进行了比较;并且指出,正是伯恩斯的这种对辞格的精细的选择,才使得他成为杰出的诗人.     

人工智能时代“语音信号处理”课程的改革

语音信号处理是电子信息类的专业选修课之一。本文在分析该课程的传统教学内容结构及特点的基础上,通过拓展理论知识和开展综合创新课程项目,探索融入新理论和新技术的教学方法。激发学生学习的积极性和主动性,培养学生关注新方法和新技术的发展,并将其应用的能力,加强团队分工合作的意识及与教师的互动,提高教与学的质量。     

广电智能语音识别系统建设实施方案

广电智能语音识别系统的建设实施,主要运用云计算相关技术手段,并配以完善的数据库体系,通过智能识别与分析对用户语音指令进行逻辑判断,进而以遥控器为媒介实现智能语音人机交互的过程。     

多阶段生成器与时频鉴别器的GAN语音增强算法

传统生成对抗网络的语音增强算法(SEGAN)将时域语音波形作为映射目标, 在低信噪比条件下, 语音时域波形会淹没在噪声中, 导致SEGAN的增强性能会急剧下降, 语音失真现象较为严重. 针对该问题, 提出了一种多阶段的时频域生成对抗网络的语音增强算法(multi-stage-time-frequency SEGAN, MS-TFSEGAN). MS-TFSEGAN采用了多阶段生成器与时频域双鉴别器的模型结构, 不断对映射结果进行完善, 同时捕获时域与频域信息. 另外, 为了进一步提升模型对频域细节信息的学习能力, MS-TFSEGAN在生成器损失函数中引入了频域L1损失. 实验证明, 在低信噪比条件下, MS-TFSEGAN的语音质量和可懂度与SEGAN相比分别提升了约13.32%和8.97%, 作为语音识别前端时在CER上实现了7.3%的相对提升.     

运营商智慧客服体系化建设

从运营商重要的语音客服热线、微信客服智能化发展出发,首先分析了传统客服面临的问题与挑战,对人工智能技术在客服领域应用的关键技术进行了客观的评估,提出人工智能与运营商客服结合的方向和体系化思考。结合某运营商现网实际情况,针对人工智能与运营商客服结合的关键技术和应用实践进行了剖析研究,推进智能语音客服、微信机器人的应用实践,提出现网改造、能力开放、智能应用体系部署解决方案。最后对人工智能在智慧客服中的应用进行了展望。     

截幅失真对低速语音编码的影响分析

截幅失真会影响语音编码质量,特别在低速率语音编码条件下,截幅语音不再符合人发声模型,编码语音质量严重下降。为了研究截幅失真对低速率语音编码的影响,从截幅语音编码参数提取和截幅语音编码质量两个方面进行了分析。采用偏离度衡量低速率语音编码参数提取的准确性,编码参数包括LPC、基音周期和清浊音。在不同截幅程度下,分析了各种参数错误分布、错误类型和错误原因。采用客观感知语音质量评估PESQ打分评估截幅语音编码质量。针对常用截幅修复方法在截幅程度较大时修复性能下降严重的现象,提出采用两种改进型截幅修复方法对截幅语音进行修复。实验结果表明,改进的截幅修复方法能有效提高截幅程度较大时的低速语音编码质量。