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The 2nd International Conference on Artificial Intelligence and Speech Technology (AIST2020) was organized by Indira Gandhi Delhi Technical University for Women, Delhi, India on November 19–20, 2020. AIST2020 is dedicated to cutting-edge research that addresses the scientific needs of academic researchers and industrial professionals to explore new horizons of knowledge related to Artificial Intelligence and Speech Technologies. AIST2020 includes high-quality paper presentation sessions revealing the latest research findings, and engaging participant discussions. The main focus is on novel contributions which would open new opportunities for providing better and low-cost solutions for the betterment of society. These include the use of new AI-based approaches like Deep Learning, CNN, RNN, GAN, and others in various Speech related issues like speech synthesis, speech recognition, etc.
This volume constitutes selected papers presented at the Third International Conference on Artificial Intelligence and Speech Technology, AIST 2021, held in Delhi, India, in November 2021. The 36 full papers and 18 short papers presented were thoroughly reviewed and selected from the 178 submissions. They provide a discussion on application of Artificial Intelligence tools in speech analysis, representation and models, spoken language recognition and understanding, affective speech recognition, interpretation and synthesis, speech interface design and human factors engineering, speech emotion recognition technologies, audio-visual speech processing and several others.
This volume constitutes selected papers presented at the Third International Conference on Artificial Intelligence and Speech Technology, AIST 2021, held in Delhi, India, in November 2021. The 36 full papers and 18 short papers presented were thoroughly reviewed and selected from the 178 submissions. They provide a discussion on application of Artificial Intelligence tools in speech analysis, representation and models, spoken language recognition and understanding, affective speech recognition, interpretation and synthesis, speech interface design and human factors engineering, speech emotion recognition technologies, audio-visual speech processing and several others.
This textbook explains Deep Learning Architecture, with applications to various NLP Tasks, including Document Classification, Machine Translation, Language Modeling, and Speech Recognition. With the widespread adoption of deep learning, natural language processing (NLP),and speech applications in many areas (including Finance, Healthcare, and Government) there is a growing need for one comprehensive resource that maps deep learning techniques to NLP and speech and provides insights into using the tools and libraries for real-world applications. Deep Learning for NLP and Speech Recognition explains recent deep learning methods applicable to NLP and speech, provides state-of-the-art approaches, and offers real-world case studies with code to provide hands-on experience. Many books focus on deep learning theory or deep learning for NLP-specific tasks while others are cookbooks for tools and libraries, but the constant flux of new algorithms, tools, frameworks, and libraries in a rapidly evolving landscape means that there are few available texts that offer the material in this book. The book is organized into three parts, aligning to different groups of readers and their expertise. The three parts are: Machine Learning, NLP, and Speech Introduction The first part has three chapters that introduce readers to the fields of NLP, speech recognition, deep learning and machine learning with basic theory and hands-on case studies using Python-based tools and libraries. Deep Learning Basics The five chapters in the second part introduce deep learning and various topics that are crucial for speech and text processing, including word embeddings, convolutional neural networks, recurrent neural networks and speech recognition basics. Theory, practical tips, state-of-the-art methods, experimentations and analysis in using the methods discussed in theory on real-world tasks. Advanced Deep Learning Techniques for Text and Speech The third part has five chapters that discuss the latest and cutting-edge research in the areas of deep learning that intersect with NLP and speech. Topics including attention mechanisms, memory augmented networks, transfer learning, multi-task learning, domain adaptation, reinforcement learning, and end-to-end deep learning for speech recognition are covered using case studies.
Intelligent Speech Signal Processing investigates the utilization of speech analytics across several systems and real-world activities, including sharing data analytics related information, creating collaboration networks between several participants, and implementing video-conferencing in different application areas. It provides a forum for readers to discover the characteristics of intelligent speech signal processing systems across different domains. Chapters focus on the latest applications of speech data analysis and management tools across different recording systems. The book emphasizes the multi-disciplinary nature of the field, presenting different applications and challenges with extensive studies on the design, implementation, development, and management of intelligent systems, neural networks, and related machine learning techniques for speech signal processing. Highlights different data analytics techniques in speech signal processing, including machine learning, and data mining Illustrates different applications and challenges across the design, implementation, and management of intelligent systems and neural networks techniques for speech signal processing Includes coverage of biomodal speech recognition, voice activity detection, spoken language and speech disorder identification, automatic speech to speech summarization, and convolutional neural networks
This volume constitutes selected papers presented at the Third International Conference on Artificial Intelligence and Speech Technology, AIST 2021, held in Delhi, India, in November 2021. The 36 full papers and 18 short papers presented were thoroughly reviewed and selected from the 178 submissions. They provide a discussion on application of Artificial Intelligence tools in speech analysis, representation and models, spoken language recognition and understanding, affective speech recognition, interpretation and synthesis, speech interface design and human factors engineering, speech emotion recognition technologies, audio-visual speech processing and several others.
An accessible explanation of the technologies that enable such popular voice-interactive applications as Alexa, Siri, and Google Assistant. Have you talked to a machine lately? Asked Alexa to play a song, asked Siri to call a friend, asked Google Assistant to make a shopping list? This volume in the MIT Press Essential Knowledge series offers a nontechnical and accessible explanation of the technologies that enable these popular devices. Roberto Pieraccini, drawing on more than thirty years of experience at companies including Bell Labs, IBM, and Google, describes the developments in such fields as artificial intelligence, machine learning, speech recognition, and natural language understanding that allow us to outsource tasks to our ubiquitous virtual assistants. Pieraccini describes the software components that enable spoken communication between humans and computers, and explains why it's so difficult to build machines that understand humans. He explains speech recognition technology; problems in extracting meaning from utterances in order to execute a request; language and speech generation; the dialog manager module; and interactions with social assistants and robots. Finally, he considers the next big challenge in the development of virtual assistants: building in more intelligence--enabling them to do more than communicate in natural language and endowing them with the capacity to know us better, predict our needs more accurately, and perform complex tasks with ease.
Spoken language understanding (SLU) is an emerging field in between speech and language processing, investigating human/ machine and human/ human communication by leveraging technologies from signal processing, pattern recognition, machine learning and artificial intelligence. SLU systems are designed to extract the meaning from speech utterances and its applications are vast, from voice search in mobile devices to meeting summarization, attracting interest from both commercial and academic sectors. Both human/machine and human/human communications can benefit from the application of SLU, using differing tasks and approaches to better understand and utilize such communications. This book covers the state-of-the-art approaches for the most popular SLU tasks with chapters written by well-known researchers in the respective fields. Key features include: Presents a fully integrated view of the two distinct disciplines of speech processing and language processing for SLU tasks. Defines what is possible today for SLU as an enabling technology for enterprise (e.g., customer care centers or company meetings), and consumer (e.g., entertainment, mobile, car, robot, or smart environments) applications and outlines the key research areas. Provides a unique source of distilled information on methods for computer modeling of semantic information in human/machine and human/human conversations. This book can be successfully used for graduate courses in electronics engineering, computer science or computational linguistics. Moreover, technologists interested in processing spoken communications will find it a useful source of collated information of the topic drawn from the two distinct disciplines of speech processing and language processing under the new area of SLU.