Advanced Algorithms in Speech Recognition
( 25 Modules )

Module #1
Introduction to Speech Recognition
Overview of speech recognition, history, and applications

Module #2
Acoustic Modeling
Introduction to acoustic models, Gaussian Mixture Models (GMMs), and Hidden Markov Models (HMMs)

Module #3
Deep Learning for Acoustic Modeling
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for acoustic modeling

Module #4
Language Modeling
Introduction to language models, n-gram models, and smoothing techniques

Module #5
Neural Language Models
Recurrent Neural Networks (RNNs) and Transformers for language modeling

Module #6
Decoding and Search Algorithms
Viterbi algorithm, beam search, and lattice-based decoding

Module #7
Advanced Decoding Techniques
A* search, lattice rescoring, and confusion network decoding

Module #8
Speaker Adaptation and Normalization
Speaker adaptation techniques and vocal tract length normalization

Module #9
Noise Robustness and Channel Compensation
Noise robustness techniques and channel compensation methods

Module #10
Multi-Microphone Speech Recognition
Beamforming andblind source separation for multi-microphone speech recognition

Module #11
Speech Enhancement and Separation
Speech enhancement and separation techniques using deep learning

Module #12
End-to-End Speech Recognition
End-to-end speech recognition models and attention-based architectures

Module #13
Attention Mechanisms in Speech Recognition
Attention mechanisms and their applications in speech recognition

Module #14
Transfer Learning and Domain Adaptation
Transfer learning and domain adaptation techniques for speech recognition

Module #15
Multitask Learning and Joint Optimization
Multitask learning and joint optimization techniques for speech recognition

Module #16
Evaluation Metrics and Techniques
Evaluation metrics and techniques for speech recognition systems

Module #17
Error Analysis and Debugging
Error analysis and debugging techniques for speech recognition systems

Module #18
Real-World Applications and Case Studies
Real-world applications and case studies of speech recognition systems

Module #19
Ethical Considerations and Fairness in Speech Recognition
Ethical considerations and fairness in speech recognition systems

Module #20
Robustness to Variations and Adversarial Attacks
Robustness to variations and adversarial attacks in speech recognition systems

Module #21
Explainability and Interpretability in Speech Recognition
Explainability and interpretability techniques for speech recognition models

Module #22
Interactive and Incremental Speech Recognition
Interactive and incremental speech recognition systems

Module #23
Multimodal Speech Recognition
Multimodal speech recognition systems incorporating computer vision and other modalities

Module #24
Low-Resource and Low-Computational Speech Recognition
Low-resource and low-computational speech recognition techniques

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

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