Reinforcement Learning Methods in Speech and Language Technology

Lin, Baihan

Reinforcement Learning Methods in Speech and Language Technology
  • 出版商: Springer
  • 出版日期: 2024-11-12
  • 售價: $3,690
  • 貴賓價: 9.5 折 $3,506
  • 語言: 英文
  • 頁數: 201
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 303153719X
  • ISBN-13: 9783031537196
  • 相關分類: Reinforcement、DeepLearning

    • 海外代購書籍(需單獨結帳)

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商品描述

This book offers a comprehensive guide to reinforcement learning (RL) and bandits methods, specifically tailored for advancements in speech and language technology. Starting with a foundational overview of RL and bandit methods, the book dives into their practical applications across a wide array of speech and language tasks. Readers will gain insights into how these methods shape solutions in automatic speech recognition (ASR), speaker recognition, diarization, spoken and natural language understanding (SLU/NLU), text-to-speech (TTS) synthesis, natural language generation (NLG), and conversational recommendation systems (CRS). Further, the book delves into cutting-edge developments in large language models (LLMs) and discusses the latest strategies in RL, highlighting the emerging fields of multi-agent systems and transfer learning.

Emphasizing real-world applications, the book provides clear, step-by-step guidance on employing RL and bandit methods to address challenges in speech and language technology. It includes case studies and practical tips that equip readers to apply these methods to their own projects. As a timely and crucial resource, this book is ideal for speech and language researchers, engineers, students, and practitioners eager to enhance the performance of speech and language systems and to innovate with new interactive learning paradigms from an interface design perspective.

商品描述(中文翻譯)

本書提供了一本全面的強化學習（RL）和賭徒方法的指南，特別針對語音和語言技術的進展而設計。從強化學習和賭徒方法的基礎概述開始，本書深入探討這些方法在各種語音和語言任務中的實際應用。讀者將了解這些方法如何塑造自動語音識別（ASR）、說話者識別、對話分離、口語和自然語言理解（SLU/NLU）、文本轉語音（TTS）合成、自然語言生成（NLG）以及對話推薦系統（CRS）中的解決方案。此外，本書還深入探討大型語言模型（LLMs）的前沿發展，並討論強化學習中的最新策略，突顯多代理系統和遷移學習等新興領域。

本書強調實際應用，提供清晰的逐步指導，幫助讀者運用強化學習和賭徒方法來解決語音和語言技術中的挑戰。書中包含案例研究和實用建議，使讀者能夠將這些方法應用於自己的項目。作為一本及時且重要的資源，本書非常適合語音和語言研究者、工程師、學生及實務工作者，幫助他們提升語音和語言系統的性能，並從介面設計的角度創新新的互動學習範式。

作者簡介

Dr. Baihan Lin is a leading researcher, neuroscientist, inventor, and professor specializing in speech and natural language processing (NLP). He holds faculty positions at the Icahn School of Medicine at Mount Sinai and Harvard University. Known for his expertise in trustworthy Neuro-AI and computational psychiatry, Dr. Lin has made significant contributions to these fields through his work at Columbia University, where he earned his PhD, and through his research at leading tech companies such as IBM, Google, Microsoft, Amazon, and BGI Genomics.

His research program focuses on developing intelligent speech and text-based systems to enhance human-AI and human-human interactions in healthcare. Notably, he developed the first-ever online and reinforcement learning (RL)-based speaker diarization system and RL-based interactive spoken language understanding (SLU) systems for children with speech and communication disorders.

Dr. Lin's work in deep learning, RL, and NLP has led to real-world applications, including AI companions for therapists and context-aware virtual realities. He has authored over 50 peer-reviewed publications and patents and has served on program committees and as a reviewer for over 15 top AI conferences and more than 20 journals. He has chaired tutorials and workshops at INTERSPEECH, ICASSP, WACV, and IJCAI, focusing on RL, human-in-the-loop language technology, and most recently, the alignment, privacy, security, and governance of generative AI.

As a finalist for the Bell Labs Prize and XPRIZE, Dr. Lin's contributions in real-time algorithms advance the understanding of the human brain, support disadvantaged individuals with mental health conditions, and drive the evolution of affective and empathetic AI in the era of large language models.

作者簡介(中文翻譯)

白漢·林博士是一位領先的研究者、神經科學家、發明家及專注於語音和自然語言處理（NLP）的教授。他在西奈山醫學院（Icahn School of Medicine at Mount Sinai）和哈佛大學擔任教職。林博士以其在可信賴的神經人工智慧（Neuro-AI）和計算精神病學方面的專業知識而聞名，並通過在哥倫比亞大學獲得博士學位的研究以及在IBM、Google、Microsoft、Amazon和BGI Genomics等領先科技公司的研究，對這些領域做出了重要貢獻。

他的研究計畫專注於開發智能語音和基於文本的系統，以增強醫療保健中的人機互動和人與人之間的互動。值得注意的是，他開發了首個基於在線和強化學習（RL）的說話者分離系統，以及針對有語言和溝通障礙的兒童的RL基於互動口語理解（SLU）系統。

林博士在深度學習、強化學習和自然語言處理方面的工作已導致實際應用，包括為治療師提供的人工智慧伴侶和上下文感知的虛擬現實。他已發表超過50篇經過同行評審的出版物和專利，並在超過15個頂尖人工智慧會議和20多本期刊擔任程序委員會成員及審稿人。他曾在INTERSPEECH、ICASSP、WACV和IJCAI等會議上主持教程和研討會，專注於強化學習、人機協作的語言技術，以及最近的生成式人工智慧的對齊、隱私、安全和治理。

作為貝爾實驗室獎和XPRIZE的決賽入圍者，林博士在實時算法方面的貢獻推進了對人腦的理解，支持有心理健康問題的弱勢群體，並推動了在大型語言模型時代情感和同理心人工智慧的演變。

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