Reinforcement Learning Methods in Speech and Language Technology
Description
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.
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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.
Content
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.
