基于感知评价的Internet语音终端流量控制

分析了影响因特网语音通信质量的主要因素，根据实时传输控制协议计算延迟、丢包率等网络参数，并利用RBF神经网络将参数映射为语音质量评分，根据评分值的变化动态地调整语音终端的编码速率，在保证一定语音质量的同时，缓解网络拥塞。实验证明了该方法的有效性。     

多带抗噪声语音识别算法研究

根据Flether等人的研究,基于感知独立性假设的子带识别方法被用于抗噪声鲁棒语音识别。本文拓展子带方法,采用基于噪声污染假定的多带框架来减少噪声影响。论文不仅从理论上分析了噪声污染假定多带框架在识别性能上的潜在优势,而且提出了多带环境下的鲁棒语音识别算法。研究表明：多带框架不仅回避了独立感知假设要求,而且与子带方法相比,多带方法能更好的减少噪声影响,提高系统识别性能。     

基于TC8830AF的智能语音卡结构与设计

本文描述一种基于８０３１单片机和ＴＣ８８３０ＡＦ语音处理器的ＰＣ机智能语音卡，通过软硬件设计解决了ＰＣ－８０３１－ＴＣ８８３０ＡＦ结构中的通讯问题。该语音卡在进行语音的合成、编辑和播放时，仅占用极少的ＰＣ机ＣＰＵ处理时间，能满足计算机系统进行实时语音合成的要求。     

Internet中文信息搜索引擎评析

介绍搜索引擎的分类、对比和查询技巧。     

MOBSY: Integration of vision and dialogue in service robots

Abstract. This contribution introduces MOBSY, a fully integrated, autonomous mobile service robot system. It acts as an automatic dialogue-based receptionist for visitors to our institute. MOBSY incorporates many techniques from different research areas into one working stand-alone system. The techniques involved range from computer vision over speech understanding to classical robotics. Along with the two main aspects of vision and speech, we also focus on the integration aspect, both on the methodological and on the technical level. We describe the task and the techniques involved. Finally, we discuss the experiences that we gained with MOBSY during a live performance at our institute.     

互联网的语音应用─—互联网电话(Internet phone)

本文对互联网电话的发展趋势，所使用的软件和关键技术作了介绍，并介绍了几家互联网电话产品。     

单麦克风和双麦克风语音增强系统的研究

本文对采用减谱法和箱关法的语音增强系统进行了研究，系统的噪声为白噪声和普通机房噪声。减谱法采用单麦克风输入，试验中发现语音的分帧方法对消噪影响很大，通过比较得到了一种具有明显消噪效果的分帧方法，在相关法中采用双麦克风输入，对两个输入信号之间的时延差进行了补偿，在求维纳滤波器系数时，对信号谱进行统计平均，从而使消噪后的语音信号具有较好的自然度。在研究以上两种方法的基础上，我们提出了一种混合消噪系统，     

利用先进的结构试验系统进行发动机构件的仿真疲劳试验

本文介绍一套先进的构件实验设备的功能及其在发动机构件疲劳试验方面的具体应用实例．通过对几种受力较复杂构件的受力分析设计出实验方法，使构件在实验室中受力情况与构件实际受力情况相似或相同，这样，得出的试验数据就可为构件的可靠性分析工作提供较好的依据．     

语音识别算法的确定与实现

在语音识别的实验中，对几种算法方案进行了比较、分析和择优淘劣，标准是在一定词汇量的条件下，权衡占用机器的内存空间、（正确）识别率和响应速度。力争使与话者有关的单词语音识别系统的设计达到优化，取得满意的结果。本文即是此项实验的总结。     

Simultaneous translation of lectures and speeches

With increasing globalization, communication across language and cultural boundaries is becoming an essential requirement of doing business, delivering education, and providing public services. Due to the considerable cost of human translation services, only a small fraction of text documents and an even smaller percentage of spoken encounters, such as international meetings and conferences, are translated, with most resorting to the use of a common language (e.g. English) or not taking place at all. Technology may provide a potentially revolutionary way out if real-time, domain-independent, simultaneous speech translation can be realized. In this paper, we present a simultaneous speech translation system based on statistical recognition and translation technology. We discuss the technology, various system improvements and propose mechanisms for user-friendly delivery of the result. Over extensive component and end-to-end system evaluations and comparisons with human translation performance, we conclude that machines can already deliver comprehensible simultaneous translation output. Moreover, while machine performance is affected by recognition errors (and thus can be improved), human performance is limited by the cognitive challenge of performing the task in real time.