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Apprentice Mode
10 Modules / ~100 pages
Wizard Mode
~25 Modules / ~400 pages

Machine Learning in Speech Recognition Systems
( 25 Modules )

Module #1
Introduction to Speech Recognition
Overview of speech recognition, its applications, and importance of machine learning in speech recognition
Module #2
Fundamentals of Machine Learning
Basics of machine learning, types of learning, and key concepts
Module #3
Speech Signal Processing
Introduction to speech signal processing, acoustic features, and pre-processing techniques
Module #4
Types of Speech Recognition Systems
Overview of different types of speech recognition systems, including rule-based, statistical, and hybrid approaches
Module #5
Machine Learning Algorithms for Speech Recognition
Introduction to machine learning algorithms used in speech recognition, including HMM, GMM, and neural networks
Module #6
Hidden Markov Models (HMMs)
In-depth study of HMMs, including architecture, training, and applications in speech recognition
Module #7
Gaussian Mixture Models (GMMs)
In-depth study of GMMs, including architecture, training, and applications in speech recognition
Module #8
Deep Learning for Speech Recognition
Introduction to deep learning, including CNNs, RNNs, and LSTMs, and their applications in speech recognition
Module #9
Convolutional Neural Networks (CNNs) for Speech Recognition
In-depth study of CNNs, including architecture, training, and applications in speech recognition
Module #10
Recurrent Neural Networks (RNNs) for Speech Recognition
In-depth study of RNNs, including architecture, training, and applications in speech recognition
Module #11
Long Short-Term Memory (LSTM) Networks for Speech Recognition
In-depth study of LSTMs, including architecture, training, and applications in speech recognition
Module #12
Speech Features and Acoustic Modeling
Overview of speech features, including MFCCs, and acoustic modeling techniques
Module #13
Language Modeling for Speech Recognition
Introduction to language modeling, including n-gram models, and applications in speech recognition
Module #14
Decoder Algorithms for Speech Recognition
Overview of decoder algorithms, including Viterbi and beam search, and their applications in speech recognition
Module #15
Evaluation Metrics for Speech Recognition
Overview of evaluation metrics, including WER, SER, and accuracy, and their applications in speech recognition
Module #16
Challenges in Speech Recognition
Overview of challenges in speech recognition, including noise robustness, speaker variability, and language modeling
Module #17
Advanced Topics in Speech Recognition
Introduction to advanced topics, including multi-modal speech recognition, and speech recognition for low-resource languages
Module #18
Real-World Applications of Speech Recognition
Overview of real-world applications, including virtual assistants, speech-to-text systems, and voice-controlled devices
Module #19
Speech Recognition Systems Development
Hands-on experience with developing a speech recognition system using popular toolkits and libraries
Module #20
Case Study:Building a Speech Recognition System
In-depth case study of building a speech recognition system, including data collection, feature extraction, and model training
Module #21
Speech Recognition for Special Populations
Overview of speech recognition for special populations, including children, seniors, and individuals with disabilities
Module #22
Ethical Considerations in Speech Recognition
Overview of ethical considerations, including privacy, security, and bias in speech recognition systems
Module #23
Future of Speech Recognition
Overview of future directions, including edge AI, and the role of speech recognition in emerging technologies
Module #24
Research Opportunities in Speech Recognition
Overview of research opportunities, including multi-modal speech recognition, and speech recognition for low-resource languages
Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning in Speech Recognition Systems career


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