Advances in Chinese Spoken Language Processing pdf epub mobi txt 电子书 下载 2026

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出版者:World Scientific Pub Co Inc

作者:Lee, Chin-hue/ Li, Haizhou/ Lee, Lin-shan/ Wang, Ren-hua/ Huo, Qiang

出品人:

页数:545

译者:

出版时间:

价格:93

装帧:HRD

isbn号码:9789812569042

丛书系列:

图书标签:

• 语音
• 论文集
• 语音处理
• 中文语音
• 自然语言处理
• 计算语言学
• 语音识别
• 语音合成
• 口语化文本
• 汉语方言
• 语音技术
• 人机交互

Advances in Chinese Spoken Language Processing: A Gateway to Understanding and Interacting with Spoken Chinese This book offers a comprehensive exploration of the rapidly evolving field of Chinese spoken language processing (CSLP). It delves into the fundamental challenges and cutting-edge advancements in enabling machines to understand, generate, and interact with the nuances of the Chinese spoken word. From the intricacies of phonetics and phonology to the complexities of syntax, semantics, and pragmatics in spoken Chinese, this volume provides a rich and detailed overview of the state-of-the-art research and development. Key areas covered within this extensive work include: I. Acoustic Modeling and Speech Recognition: Phonetic and Phonological Foundations: A deep dive into the acoustic properties of Mandarin Chinese, including its unique tonal system, syllable structure, and common phonetic variations. This section will dissect the challenges posed by dialectal differences, spontaneous speech phenomena (like coarticulation, disfluencies, and assimilation), and the impact of background noise on speech recognition accuracy. Acoustic Feature Extraction: A thorough examination of various techniques for extracting meaningful acoustic features from speech signals, such as Mel-frequency cepstral coefficients (MFCCs), perceptual linear prediction (PLP), and more recent deep learning-based features. The book will discuss the strengths and weaknesses of each approach in the context of Chinese speech. Acoustic Model Architectures: Comprehensive coverage of traditional and modern acoustic modeling techniques, including hidden Markov models (HMMs), Gaussian mixture models (GMMs), and the dominant role of deep neural networks (DNNs). Detailed explanations of architectures like deep belief networks (DBNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and convolutional neural networks (CNNs) as applied to acoustic modeling for Chinese will be provided. The latest advancements in end-to-end acoustic modeling will also be a significant focus. Data Augmentation and Training Strategies: Practical insights into effective data augmentation techniques to improve the robustness of acoustic models, especially in low-resource scenarios or for specific dialects. The book will also explore various training strategies and optimization methods tailored for large-scale Chinese speech datasets. II. Language Modeling and Spoken Language Understanding: Lexical and Syntactic Modeling: An in-depth analysis of how to model the probabilistic relationships between words and phrases in spoken Chinese. This includes discussion of n-gram models, their limitations, and the paradigm shift towards neural language models. Advanced topics such as sub-word units, character-based modeling, and context-aware language modeling will be explored. Semantic Role Labeling and Word Sense Disambiguation: Addressing the critical task of understanding the meaning conveyed by spoken language. This section will detail approaches to identify semantic roles of constituents in a sentence and resolve ambiguity in word meanings, crucial for accurate comprehension of Chinese. Discourse Processing and Pragmatics: Moving beyond sentence-level understanding, this part of the book examines how to model the flow of conversation, identify discourse markers, and interpret the speaker's intent and underlying meaning. The impact of context, implicit information, and cultural nuances in spoken Chinese will be a central theme. Spontaneous Speech Phenomena and Their Impact: A dedicated exploration of how disfluencies (e.g., fillers, repetitions, false starts), hesitation phenomena, and other characteristics of natural speech affect language understanding and the development of robust language models. Techniques for detecting and handling these phenomena will be discussed. III. Speech Synthesis and Generation: Text-to-Speech (TTS) Systems for Chinese: A comprehensive overview of the pipeline for generating natural-sounding speech from Chinese text. This includes the crucial steps of text normalization, grapheme-to-phoneme (G2P) conversion, prosody prediction, and waveform generation. Acoustic and Prosodic Modeling for Synthesis: Detailed discussion of how acoustic and prosodic features (pitch, duration, intensity) are modeled and synthesized to create expressive and human-like speech. The role of emotion and style in spoken Chinese synthesis will be investigated. Deep Learning Approaches to Speech Synthesis: In-depth coverage of modern end-to-end TTS systems, including parametric synthesis methods like Tacotron, Transformer-TTS, and GAN-based approaches. The book will analyze the advantages of these methods in terms of naturalness and controllability. Voice Conversion and Speaker Adaptation: Exploring techniques for modifying the characteristics of synthesized speech, such as changing the speaker's voice or adapting the synthesis to specific speaking styles or emotional states. IV. Applications and Future Directions: Real-World Applications: Demonstrating the practical utility of CSLP technologies across a wide range of domains, including but not limited to: Voice Assistants and Conversational AI: Enabling natural human-computer interaction through spoken dialogue. Speech Translation: Bridging language barriers with real-time spoken language translation. Automatic Speech Recognition for Broadcast Media and Education: Transcribing lectures, news, and other audio content. Healthcare Applications: Voice-enabled medical documentation and patient interaction. Accessibility Tools: Assisting individuals with communication challenges. Emerging Trends and Challenges: Looking towards the future, the book will discuss promising research directions, including: Low-Resource Spoken Language Processing: Developing effective techniques for dialects or languages with limited data. Multimodal Spoken Language Processing: Integrating visual cues (e.g., lip movements) with audio for improved understanding. Personalized and Context-Aware Spoken Language Processing: Tailoring systems to individual users and specific conversational contexts. Ethical Considerations and Bias Mitigation: Addressing fairness, privacy, and potential biases in CSLP systems. This book is an invaluable resource for researchers, engineers, and students interested in the intricacies of spoken Chinese and the development of advanced spoken language processing technologies. It provides a solid theoretical foundation, an in-depth understanding of current methodologies, and a clear vision for the future of this dynamic field. Whether you are seeking to build more intelligent conversational agents, enhance speech recognition accuracy, or create more natural speech synthesis, this comprehensive volume will equip you with the knowledge and insights needed to succeed.

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