Speech To Text Systems And Technologies
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Speech Technology
Author: Fang Chen
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-07-01
This book gives an overview of the research and application of speech technologies in different areas. One of the special characteristics of the book is that the authors take a broad view of the multiple research areas and take the multidisciplinary approach to the topics. One of the goals in this book is to emphasize the application. User experience, human factors and usability issues are the focus in this book.
Speech Recognition & Synthesis: Concepts, Technologies, and Applications
Table of Contents Introduction to Speech Technologies What is Speech Recognition? What is Speech Synthesis? History of Speech Technologies Applications in Modern Technology Key Concepts in Speech Processing The Basics of Speech Recognition Understanding Speech and Language Acoustic Models: Sound to Signal Language Models: From Words to Meaning Features Extraction Machine Learning Techniques in Speech Recognition The Challenges of Speech Recognition Variability in speech: Accents, Noises, Context The Recognition Pipeline: From Sound to Text The Basics of Speech Synthesis What is Text-to-Speech (TTS)? The Process of Generating Speech Unit Selection and Concatenative Synthesis Parametric Synthesis (HMM-based, Deep Learning models) Modern Approaches: WaveNet and Neural Networks Pros and Cons of Different Synthesis Techniques Applications of Speech Synthesis Key Technologies Behind Speech Recognition Signal Processing Techniques Hidden Markov Models (HMM) Neural Networks and Deep Learning in Speech Recognition End-to-End Systems in Speech Recognition Popular Speech Recognition Systems: Google, Siri, Alexa Key Technologies Behind Speech Synthesis Speech Signal Representation Text Preprocessing for TTS Synthesis Models: Statistical Parametric, Deep Learning, and Hybrid Models Voice Quality and Naturalness in TTS The Role of Prosody in TTS Speech Recognition and Synthesis in Real-World Applications Virtual Assistants and Smart Speakers Voice Search and Dictation Systems Accessibility Tools (e.g., Screen Readers, Voice Commands) Speech-based Translation Systems Healthcare (Speech-to-Text in Medical Records, Assistive Technologies) Speech in Automotive Systems and IoT Devices Advanced Topics in Speech Recognition Speaker Recognition and Adaptation Multilingual Speech Recognition Noise Robustness in Speech Recognition Real-Time Recognition and Low-Latency Systems Challenges of Speech Recognition in Unstructured Environments Advanced Topics in Speech Synthesis Emotional Speech Synthesis Expressiveness and Personalization in TTS Custom Voice Generation Prosody Control and Natural Sounding Speech Challenges in Generating Natural Speech Speech Recognition and Synthesis in AI and NLP Integrating Speech Recognition with Natural Language Processing (NLP) Speech as Input in Dialogue Systems Conversational AI and Virtual Agents Transfer Learning and Fine-Tuning Models for Speech Ethical Considerations and Challenges Privacy and Data Security in Speech Systems Bias and Fairness in Speech Recognition Misuse of Speech Technologies (e.g., Deepfakes, Impersonation) Accessibility and Inclusivity Issues The Future of Speech Recognition and Synthesis The Role of AI and Machine Learning Multimodal Systems (Speech + Gesture + Vision) Advances in Real-Time Systems Voice Cloning and Deepfake Technologies The Road Ahead for Natural Language Interfaces Conclusion Summary of Key Concepts and Technologies The Impact of Speech Technologies on Society Future Research Directions
Speech Recognition & Synthesis: Theory, Technology, and Applications
Table of Contents Introduction to Speech Technologies Overview of Speech Recognition & Synthesis Historical Background and Evolution Key Terminologies Applications and Use Cases Fundamentals of Speech Recognition Acoustic Model Language Model Feature Extraction Signal Processing Techniques Speech Recognition Techniques Traditional Methods (Hidden Markov Models, etc.) Deep Learning Approaches End-to-End Models Voice Activity Detection (VAD) Phoneme Recognition and Transcription Speech Synthesis: An Overview Text-to-Speech (TTS) System Architecture Types of Speech Synthesis Concatenative Synthesis Parametric Synthesis Neural Network-based Synthesis (WaveNet, Tacotron, etc.) Signal Processing in Speech Digital Signal Processing (DSP) Fundamentals Spectrogram and Mel-frequency Cepstral Coefficients (MFCC) Preprocessing Techniques Noise Reduction and Echo Cancellation Deep Learning in Speech Technologies Convolutional Neural Networks (CNNs) for Speech Recognition Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks Transformer Models in Speech Recognition and Synthesis Generative Adversarial Networks (GANs) in Speech Synthesis Natural Language Processing (NLP) for Speech Speech Recognition and NLP Integration Named Entity Recognition (NER) and Intent Detection Dialogue Systems and Conversational AI Contextual Understanding in Speech Applications Speech Recognition and Synthesis Systems Open-Source and Commercial Speech Recognition Tools Kaldi DeepSpeech Google Speech-to-Text Microsoft Azure Speech API Speech Synthesis Tools and Frameworks eSpeak Festival Google Cloud Text-to-Speech Amazon Polly Challenges in Speech Recognition Accents and Dialects Noise and Environmental Challenges Real-time Processing Language Barriers Multimodal Interaction Challenges Challenges in Speech Synthesis Naturalness vs. Clarity Emotional Tone and Expressiveness Multilingual Synthesis Data Scarcity and Collection Issues Ethical Considerations and Privacy Voice Biometrics and Security Concerns Ethical Use of Speech Data Speech Data Privacy and Anonymity Accessibility and Inclusion Applications of Speech Recognition & Synthesis Virtual Assistants (Siri, Alexa, Google Assistant) Healthcare Applications (Speech-to-Text for Doctors, Assistive Technologies) Automotive Industry (Voice-activated Navigation Systems) Smart Home Automation Language Learning Tools Future Trends in Speech Technologies Multilingual and Multimodal Speech Recognition Real-Time Synthesis and Interactive Voice Applications Voice-based Emotion Recognition Advances in Neural TTS (Text-to-Speech) Systems Integration with Other AI Technologies Conclusion Summary of Key Concepts Emerging Research Areas The Future of Speech Recognition & Synthesis