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
