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Trainable prosodic model for standard Chinese Text-to-Speech system

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Chinese Journal of Acoustics 2001年第3期20卷 257-265页

作者： TAO Jianhua, cai lianhong, ZHAO Shixia (department of computer science and technology tsinghua university Beijing 100084) department of computer science and technology tsinghua university Beijing 100084

Putonghua prosody is characterized by its hierarchical structure when influenced by linguistic environments. Based on this, a neural network, with specially weighted factors and optimizing outputs, is described and applied to construct the Putonghua prosodic model in Text-to-Speech (TTS) system. Extensive tests show that the structure of the neural network characterizes the Putonghua prosody more exactly than traditional models. Learning rate is speeded up and computational precision is improved, which makes the whole prosodic model more efficient. Furthermore, the paper also stylizes the Putonghua syllable pitch contours with SPiS parameters (Syllable Pitch Stylized Parameters), and analyzes them in adjusting the syllable pitch. It shows that the SPiS parameters effectively characterize the Putonghua syllable pitch contours, and facilitate the establishment of the network model and the prosodic controlling.

关键词： Trainable prosodic model for standard Chinese Text-to-Speech system Text

A New Rule-based Method of Automatic Phonetic Notation on Polyphones

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A New Rule-based Method of Automatic Phonetic Notation on Po...

第七届国际信号处理会议

作者： ZHENG Min cai lianhong (department of computer science and technology of tsinghua university Beijing 100084)

In this paper, a new rule-based method of automatic phonetic notation on the 220 polyphones whose appearing frequency exceeds 99% is proposed. Firstly, all the polyphones in a sentence are classifi

关键词： grapheme-phoneme conversion polyphone prosodic word prosodic functional part of speech feature extracting

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电脑电话集成系统设计中的多路控制技术

计算机工程 1999年第4期25卷 20-22页

作者：余华云蔡莲红湖北省荆州教育学院计算机管理中心荆州434001 清华大学计算机科学与技术系信息处理组北京100084

着重介绍了电脑电话集成系统设计中的两种多线路同时控制技术以及该技术在实际开发应用中应注意的一些问题。

关键词：计算机电话集成系统系统设计多路控制

Few-Shot Graph Classification with Structural-Enhanced Contrastive Learning for Graph Data Copyright Protection

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tsinghua science and technology 2024年第2期29卷 605-616页

作者： Kainan Zhang DongMyung Shin Daehee Seo Zhipeng cai department of computer science Georgia State UniversityAtlantaGA 30303USA LSWare Inc. Seoul 08504Republic of Korea College of Intelligence Information Engineering Sangmyung UniversitySeoul 03016Republic of Korea

Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training ***,not all open-source licenses may be appropriate for data sharing,as some may not provide adequate protections for sensitive or personal information such as social network ***,some data may be subject to legal or regulatory restrictions that limit its sharing,regardless of the licensing model ***,obtaining large amounts of labeled data can be difficult,time-consuming,or expensive in many real-world ***-shot graph classification,as one application of meta-learning in supervised graph learning,aims to classify unseen graph types by only using a small amount of labeled ***,the current graph neural network methods lack full usage of graph structures on molecular graphs and social network *** structural features are known to correlate with molecular properties in chemistry,structure information tends to be ignored with sufficient property information ***,the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel ***,this paper focuses on the graph classification tasks of a social network,whose complex topology has an uncertain relationship with its nodes'*** two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research,we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological *** experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.

关键词： few-shot learning contrastive learning data copyright protection

Lightweight Super-Resolution Model for Complete Model Copyright Protection

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tsinghua science and technology 2024年第4期29卷 1194-1205页

作者： Bingyi Xie Honghui Xu YongJoon Joe Daehee Seo Zhipeng cai department of computer science Georgia State UniversityAtlantaGA 30303USA LSWare Inc. Seoul 08504Republic of Korea College of Intelligence Information Engineering Sangmyung UniversityRepublic of KoreaSeoul 03016

Deep learning based techniques are broadly used in various applications, which exhibit superior performance compared to traditional methods. One of the mainstream topics in computer vision is the image super-resolution task. In recent deep learning neural networks, the number of parameters in each convolution layer has increased along with more layers and feature maps, resulting in better image super-resolution performance. In today’s era, numerous service providers offer super-resolution services to users, providing them with remarkable convenience. However, the availability of open-source super-resolution services exposes service providers to the risk of copyright infringement, as the complete model could be vulnerable to leakage. Therefore, safeguarding the copyright of the complete model is a non-trivial concern. To tackle this issue, this paper presents a lightweight model as a substitute for the original complete model in image super-resolution. This research has identified smaller networks that can deliver impressive performance, while protecting the original model’s copyright. Finally, comprehensive experiments are conducted on multiple datasets to demonstrate the superiority of the proposed approach in generating super-resolution images even using lightweight neural networks.

关键词： lightweight adversarial learning image super-resolution copyright protection

Solving Multi-Objective Vehicle Routing Problems with Time Windows: A Decomposition-Based Multiform Optimization Approach

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tsinghua science and technology 2024年第2期29卷 305-324页

作者： Yiqiao cai Zifan Lin Meiqin Cheng Peizhong Liu Ying Zhou Xiamen Key Laboratory of Data Security and Blockchain technology College of Computer Science and TechnologyHuaqiao UniversityXiamen361021China College of Engineering Huaqiao UniversityQuanzhou362000China School of computer sciences Shenzhen Institute of Information TechnologyShenzhen518000China

In solving multi-objective vehicle routing problems with time windows (MOVRPTW),most existing algorithms focus on the optimization of a single problem formulation. However,little effort has been devoted to exploiting valuable knowledge from the alternate formulations of MOVRPTW for better optimization performance. Aiming at this insufficiency,this study proposes a decomposition-based multi-objective multiform evolutionary algorithm (MMFEA/D),which performs the evolutionary search on multiple alternate formulations of MOVRPTW simultaneously to complement each other. In particular,the main characteristics of MMFEA/D are three folds. First,a multiform construction (MFC) strategy is adopted to construct multiple alternate formulations,each of which is formulated by grouping several adjacent subproblems based on the decomposition of MOVRPTW. Second,a transfer reproduction (TFR) mechanism is designed to generate offspring for each formulation via transferring promising solutions from other formulations,making that the useful traits captured from different formulations can be shared and leveraged to guide the evolutionary search. Third,an adaptive local search (ALS) strategy is developed to invest search effort on different alternate formulations as per their usefulness for MOVRPTW,thus facilitating the efficient allocation of computational resources. Experimental studies have demonstrated the superior performance of MMFEA/D on the classical Solomon instances and the real-world instances.

关键词： MOVRPTW multiform optimization multiform construction knowledge transfer adaptive local search

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基于层级策略的连续数字串识别的研究

计算机工程与应用 2003年第21期39卷 83-86页

作者：汤霖蔡莲红江门教育学院计算机系广东江门529000 清华大学计算机系北京100084

在分析汉语数字串语音特点的基础上,设计出了基于层级策略的连续数字串识别系统。该系统先对连续数字串进行确定性的预分割,再用LevelBuilding算法对每个分割段进行基于模板模糊分组的识别,在该识别结果的基础上利用加权矩阵识别算法进... 详细信息

在分析汉语数字串语音特点的基础上,设计出了基于层级策略的连续数字串识别系统。该系统先对连续数字串进行确定性的预分割,再用LevelBuilding算法对每个分割段进行基于模板模糊分组的识别,在该识别结果的基础上利用加权矩阵识别算法进一步区分易混淆语音对。该系统在计算时间减少到原来的35.2%的同时识别率提高到94.08%。

关键词：语音识别汉语数字串分割层级策略连续数字串识别系统

A DYNAMIC VISEME MODEL FOR PERSONALIZING A TALKING HEAD

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A DYNAMIC VISEME MODEL FOR PERSONALIZING A TALKING HEAD

An O（nloglogn） algorithm for Evaluation of Bounded Slice-line Grid

2002年第6届信号处理国际会议

作者： WANG Zhiming cai lianhong AI Haizhoudepartment of computer science and technology tsinghua university State Key Laboratory of Intelligent technology and Systems Beijing 100084 China

＜正＞ Personalizing a talking head means not only to personalize a head model but also to personalize his talking manner. In this paper, we propose a dynamic viseme model for visual speech synthesis that can deal with co-articulation problem and various pauses in continuous speech. Facial Animation Parameters （FAPs） defined in MPEG-4 are estimated according to the tracked feature points from two orthogonal views via a mirror setup. Individual talking manner described by model parameters is learnt from FAP data to implement a personalized talking head.

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An O（nloglogn） algorithm for Evaluation of Bounded Slice-l...