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Reinforcement learning methods in speech and language technology

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Reinforcement learning methods in speech and language technologyby Baihan Lin.
作者:	Lin, Baihan.
出版者:	Cham :Springer Nature Switzerland :2025.
面頁冊數:	xvi, 202 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Reinforcement learning.
電子資源:	https://doi.org/10.1007/978-3-031-53720-2
ISBN:	9783031537202$q(electronic bk.)

Reinforcement learning methods in speech and language technology
Lin, Baihan.

Reinforcement learning methods in speech and language technology[electronic resource] /by Baihan Lin. - Cham :Springer Nature Switzerland :2025. - xvi, 202 p. :ill. (some col.), digital ;24 cm. - Signals and communication technology,1860-4870. - Signals and communication technology..

Part I. A New Learning Paradigm in Speech and Language Technology -- Chapter 1. RL+SLT: Emerging RL-Powered Speech and Language Technologies -- Chapter 2. Why is RL+SLT Important, Now and How? -- Part II. Bandits and Reinforcement Learning: A Gentle Introduction -- Chapter 3. Introduction to the Bandit Problems -- Chapter 4. Reinforcement Learning: Preliminaries and Terminologies -- Chapter 5. The RL Toolkit: A Spectrum of Algorithms -- Chapter 6. Inverse Reinforcement Learning Problem -- Chapter 7. Behavioral Cloning and Imitation Learning -- Part III. Reinforcement Learning in SLT Applications -- Chapter 8. Reinforcement Learning Formulations for Speech and Language Applications -- Chapter 9. Reinforcement Learning in Automatic Speech Recognition (ASR): The Voice-First Revolution -- Chapter 10. Reinforcement Learning in Speaker Recognition and Diarization: Decoding the Voices in the Crowd -- Chapter 11. Reinforcement Learning in Natural Language Understanding (NLU): Teaching Machines to Comprehend -- Chapter 12. Reinforcement Learning in Spoken Language Understanding (SLU): Giving Machines an Ear for Understanding -- Chapter 13. Reinforcement Learning in Text-to-Speech (TTS) Synthesis: Giving Machines a Voice -- Chapter 14. Reinforcement Learning in Natural Language Generation (NLG): Machines as Wordsmiths -- Chapter 15. Reinforcement Learning in Large Language Models (LLM): The Rise of AI Language Giants -- Chapter 16. Reinforcement Learning in Conversational Recommendation Systems (CRS): AI's Personal Touch -- Part IV. Advanced Topics and Future Avenues -- Chapter 17. Emerging Strategies in Reinforcement Learning Methods -- Chapter 18. Navigating the Frontiers: Key Challenges and Opportunities in RL-Powered Speech and Language Technology -- Chapter 19. Reflections, Resources, and Future Horizons in RL+SLT.

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. Provides a comprehensive survey of reinforcement learning methods tailored to speech and language technology; Discusses real-world application studies such as ASR, TTS, large language models, and conversational systems; Covers emerging trends in deep reinforcement learning, multi-agent systems, and transfer learning.

ISBN: 9783031537202$q(electronic bk.)

Standard No.: 10.1007/978-3-031-53720-2doiSubjects--Topical Terms:

349131
Reinforcement learning.

LC Class. No.: Q325.6

Dewey Class. No.: 006.31

Reinforcement learning methods in speech and language technology
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505 0 $a Part I. A New Learning Paradigm in Speech and Language Technology -- Chapter 1. RL+SLT: Emerging RL-Powered Speech and Language Technologies -- Chapter 2. Why is RL+SLT Important, Now and How? -- Part II. Bandits and Reinforcement Learning: A Gentle Introduction -- Chapter 3. Introduction to the Bandit Problems -- Chapter 4. Reinforcement Learning: Preliminaries and Terminologies -- Chapter 5. The RL Toolkit: A Spectrum of Algorithms -- Chapter 6. Inverse Reinforcement Learning Problem -- Chapter 7. Behavioral Cloning and Imitation Learning -- Part III. Reinforcement Learning in SLT Applications -- Chapter 8. Reinforcement Learning Formulations for Speech and Language Applications -- Chapter 9. Reinforcement Learning in Automatic Speech Recognition (ASR): The Voice-First Revolution -- Chapter 10. Reinforcement Learning in Speaker Recognition and Diarization: Decoding the Voices in the Crowd -- Chapter 11. Reinforcement Learning in Natural Language Understanding (NLU): Teaching Machines to Comprehend -- Chapter 12. Reinforcement Learning in Spoken Language Understanding (SLU): Giving Machines an Ear for Understanding -- Chapter 13. Reinforcement Learning in Text-to-Speech (TTS) Synthesis: Giving Machines a Voice -- Chapter 14. Reinforcement Learning in Natural Language Generation (NLG): Machines as Wordsmiths -- Chapter 15. Reinforcement Learning in Large Language Models (LLM): The Rise of AI Language Giants -- Chapter 16. Reinforcement Learning in Conversational Recommendation Systems (CRS): AI's Personal Touch -- Part IV. Advanced Topics and Future Avenues -- Chapter 17. Emerging Strategies in Reinforcement Learning Methods -- Chapter 18. Navigating the Frontiers: Key Challenges and Opportunities in RL-Powered Speech and Language Technology -- Chapter 19. Reflections, Resources, and Future Horizons in RL+SLT.
520 $a 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. Provides a comprehensive survey of reinforcement learning methods tailored to speech and language technology; Discusses real-world application studies such as ASR, TTS, large language models, and conversational systems; Covers emerging trends in deep reinforcement learning, multi-agent systems, and transfer learning.
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