Speech Recognition and Synthesis
( 25 Modules )

Module #1
Introduction to Speech Processing
Overview of speech processing, importance, and applications

Module #2
Human Speech Production and Perception
Anatomy and physiology of speech production, speech acoustics, and psychoacoustics

Module #3
Speech Signal Representation
Time-domain and frequency-domain representations of speech signals

Module #4
Speech Feature Extraction
Introduction to speech feature extraction, Mel-Frequency Cepstral Coefficients (MFCCs), and filterbanks

Module #5
Speech Recognition Fundamentals
Basic concepts of speech recognition, pattern recognition, and machine learning

Module #6
Hidden Markov Models (HMMs) for Speech Recognition
Introduction to HMMs, HMM architecture, and HMM training

Module #7
Gaussian Mixture Models (GMMs) for Speech Recognition
Introduction to GMMs, GMM architecture, and GMM training

Module #8
Deep Neural Networks (DNNs) for Speech Recognition
Introduction to DNNs, DNN architectures, and DNN training for speech recognition

Module #9
Convolutional Neural Networks (CNNs) for Speech Recognition
Introduction to CNNs, CNN architectures, and CNN training for speech recognition

Module #10
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks for Speech Recognition
Introduction to RNNs, LSTMs, and their applications in speech recognition

Module #11
Speech Recognition Systems
Overview of speech recognition systems, speech recognition pipelines, and system evaluation metrics

Module #12
Language Modeling for Speech Recognition
Introduction to language modeling, n-gram models, and recurrent neural network language models

Module #13
Speech Enhancement and Noise Robustness
Speech enhancement techniques, noise robustness, and noise reduction methods

Module #14
Speech Synthesis Fundamentals
Basic concepts of speech synthesis, text-to-speech synthesis, and articulatory synthesis

Module #15
Statistical Speech Synthesis
Hidden semi-Markov models, decision trees, and clustering for speech synthesis

Module #16
WaveNet and Deep Learning for Speech Synthesis
Introduction to WaveNet, convolutional neural networks, and recurrent neural networks for speech synthesis

Module #17
Vocal Tract Modeling and Articulatory Synthesis
Introduction to vocal tract modeling, articulatory synthesis, and vocal tract area functions

Module #18
Speech Synthesis Systems
Overview of speech synthesis systems, system evaluation metrics, and applications

Module #19
Emotional and Expressive Speech Synthesis
Introduction to emotional and expressive speech synthesis, prosody modification, and voice conversion

Module #20
Multimodal Speech Processing
Introduction to multimodal speech processing, audio-visual speech recognition, and lip-reading

Module #21
Speech Recognition in Real-World Applications
Applications of speech recognition in various domains, such as virtual assistants, voice-controlled systems, and healthcare

Module #22
Speech Synthesis in Real-World Applications
Applications of speech synthesis in various domains, such as text-to-speech systems, audiobooks, and announcements

Module #23
Ethics and Fairness in Speech Recognition and Synthesis
Ethical considerations, bias, and fairness in speech recognition and synthesis systems

Module #24
Future Directions and Trends in Speech Recognition and Synthesis
Advancements, challenges, and future directions in speech recognition and synthesis research

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Speech Recognition and Synthesis career

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Speech Recognition and Synthesis ( 25 Modules )

Speech Recognition and Synthesis
( 25 Modules )