Reinforcement Learning Methods in Speech and Language Technology [Hardcover]

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.

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 Lel³ã