Automatic speech recognition. A deep learning approach

Preface
Acknowledgments
Glossary
Introducing Dialogue Systems
	What is a Dialogue System?
		Why Develop a Dialogue System?
	A Brief History of Dialogue Systems
		Text-Based and Spoken Dialogue Systems
		Voice User Interfaces
		Chatbots
		Embodied Conversational Agents
		Robots and Situated Agents
		Limitations of Early Dialogue Systems
	Present-Day Dialogue Systems
		Dialogue Systems on Messaging Platforms
		Dialogue Systems on Smartphones
		Dialogue Systems on Smart Speakers and Other Devices
		Dialogue Systems in Cars
		How Current Dialogue Systems Are Different
	Modeling Conversation in Dialogue Systems
		User-Initiated Dialogues
		System-Directed Dialogue
		Multi-Turn Open-Domain Dialogue
	Designing and Developing Dialogue Systems
Rule-Based Dialogue Systems: Architecture, Methods, and Tools
	A Typical Dialogue Systems Architecture
		Automatic Speech Recognition (ASR)
		Natural Language Understanding (NLU)
		Dialogue Management
		Natural Language Generation (NLG)
		Text-to-Speech Synthesis (TTS)
	Designing a Dialogue System
	Tools for Developing Dialogue Systems
		Visual Design Tools
		Scripting Tools for Handcrafting Dialogue Systems
		Advanced Toolkits and Frameworks
		Research-Based Toolkits
		Which is the Best Toolkit?
	Rule-Based Techniques in Dialogue Systems Participating in the Alexa Prize
Statistical Data-Driven Dialogue Systems
	Motivating the Statistical Data-Driven Approach
	Dialogue Components in the Statistical Data-Driven Approach
		Natural Language Understanding
		Dialogue Management
		Natural Language Generation
	Reinforcement Learning (RL)
		Representing Dialogue as a Markov Decision Process
		From MDPs to POMDPs
		Dialogue State Tracking
		Dialogue Policy
		Problems and Issues with Reinforcement Learning and POMDPs
Evaluating Dialogue Systems
	How to Conduct the Evaluation
		Laboratory Studies vs. Evaluations in the Wild
	Evaluating Task-Oriented Dialogue Systems
		Quantitative Metrics for Overall Dialogue System Evaluation
		Quantitative Metrics for the Evaluation of the Sub-Components of Dialogue Systems
		Qualitative/Subjective Evaluation
	Evaluating Open-Domain Dialogue Systems
		Evaluation at the Level of the Exchange
		Evaluation at the Level of the Dialogue
		ChatEval: A Toolkit for Chatbot Evaluation
		Evaluations in Challenges and Competitions
	Evaluation Frameworks
		PARADISE
		Quality of Experience (QoE)
		Interaction Quality
	What is the Best Way to Evaluate Dialogue Systems?
End-to-End Neural Dialogue Systems
	Neural Network Approaches to Dialogue Modeling
	A Neural Conversational Model
	Introduction to the Technology of Neural Dialogue
		Word Embeddings
		Recurrent Neural Networks (RNNs)
		Long Short-Term Memory Units
		The Encoder-Decoder Network
	Retrieval-Based Response Generation
	Task-Oriented Neural Dialogue Systems
	Open-Domain Neural Dialogue Systems
		Alexa Prize 2020
		Google\'s Meena
		Facebook\'s BlenderBot
		OpenAI\'s GPT-3
	Some Issues and Current Solutions
		Semantic Inconsistency
		Affect
	Dialogue Systems: Datasets, Competitions, Tasks, and Challenges
		Datasets and Corpora
		Competitions, Tasks, and Challenges
	Additional Readings
Challenges and Future Directions
	Multimodality in Dialogue
		Multimodal Fusion
		Multimodal Fission
		Multimodality in Smartphones and Smart Speakers with Displays
	Visual Dialogue and Visually Grounded Language
	Data Efficiency: Training Dialogue Systems with Sparse Data
	Knowledge Graphs for Dialogue Systems
	Reasoning and Collaborative Problem Solving in Dialogue Systems
	Discourse and Dialogue Phenomena
		Making Reference
		Detecting, Maintaining, and Changing Topic
		Multi-Party Dialogue
		Incremental Processing in Dialogue
		Turn-Taking in Dialogue
	Hybrid Dialogue Systems
	Dialogue with Social Robots
	Dialogue and the Internet of Things
	Social and Ethical Issues
	The Way Ahead
Toolkits for Developing Dialogue Systems
Bibliography
Author\'s Biography