Machine Learning for Speech Recognition
( 20 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
Introduction to machine learning concepts, types of machine learning, and popular machine learning libraries

Module #3
Acoustic Features Extraction
Introduction to acoustic features, types of features (MFCC, spectrogram, etc.), and feature extraction techniques

Module #4
Speech Preprocessing
Techniques for preprocessing speech data, including noise reduction, normalization, and silence removal

Module #5
Introduction to Deep Learning
Overview of deep learning, neural networks, and popular deep learning frameworks (TensorFlow, PyTorch)

Module #6
Convolutional Neural Networks (CNNs) for Speech
Application of CNNs to speech recognition, including architectures and techniques

Module #7
Recurrent Neural Networks (RNNs) for Speech
Application of RNNs to speech recognition, including architectures and techniques

Module #8
Long Short-Term Memory (LSTM) Networks
In-depth look at LSTM networks, their strengths, and applications in speech recognition

Module #9
Speech Recognition Architectures
Overview of popular speech recognition architectures, including HMM-DNN, CNN-RNN, and sequence-to-sequence models

Module #10
Language Modeling
Introduction to language modeling, including n-gram models, Markov models, and neural network-based models

Module #11
Decoder Techniques
Overview of decoder techniques, including beam search, prefix search, and lattice-based decoding

Module #12
Evaluation Metrics for Speech Recognition
Introduction to evaluation metrics for speech recognition, including WER, SER, and PER

Module #13
Handling Noisy Speech
Techniques for handling noisy speech, including noise reduction, robust features, and noise-aware training

Module #14
Handling Variability in Speech
Techniques for handling variability in speech, including speaker adaptation, accent recognition, and dialect recognition

Module #15
End-to-End Speech Recognition
Overview of end-to-end speech recognition models, including sequence-to-sequence models and attention-based models

Module #16
Advanced Topics in Speech Recognition
Discussion of advanced topics, including transfer learning, multi-task learning, and attention-based models

Module #17
Real-World Applications of Speech Recognition
Overview of real-world applications of speech recognition, including virtual assistants, voice-to-text systems, and speech-to-text systems

Module #18
Challenges and Future Directions
Discussion of challenges and future directions in speech recognition, including handling unknown words, out-of-vocabulary words, and low-resource languages

Module #19
Project Development
Guided project development, where students apply concepts learned throughout the course to build a speech recognition system

Module #20
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning for Speech Recognition career

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Machine Learning for Speech Recognition ( 20 Modules )

Machine Learning for Speech Recognition
( 20 Modules )