Module #1 Introduction to Speech Processing Overview of speech processing, importance of machine learning in speech processing, and course objectives
Module #2 Fundamentals of Speech Signals Basic properties of speech signals, acoustic features, and signal processing techniques
Module #3 Speech Signal Representation Time-frequency representations, spectrograms, and mel-frequency cepstral coefficients (MFCCs)
Module #4 Introduction to Machine Learning Basics of machine learning, types of machine learning, and supervised/unsupervised learning
Module #5 Supervised Learning for Speech Classification Introduction to speech classification, feature extraction, and classification algorithms
Module #6 Speech Recognition Fundamentals Introduction to automatic speech recognition (ASR), language models, and acoustic models
Module #7 Deep Learning for Speech Recognition Introduction to deep learning for ASR, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)
Module #8 Speech Enhancement Introduction to speech enhancement, noise reduction, and dereverberation techniques
Module #9 Speech Feature Extraction MFCCs, filterbanks, and other feature extraction techniques for speech processing
Module #10 Audio-Visual Speech Processing Introduction to audio-visual speech processing, lip reading, and multimodal fusion
Module #11 Speech Emotion Recognition Introduction to speech emotion recognition, emotion classification, and emotion detection
Module #12 Speaker Recognition Introduction to speaker recognition, speaker identification, and speaker verification
Module #13 Unsupervised Learning for Speech Introduction to unsupervised learning for speech, clustering, and dimensionality reduction
Module #14 Deep Learning Architectures for Speech Introduction to deep learning architectures for speech, convolutional neural networks (CNNs), and recurrent neural networks (RNNs)
Module #15 Transfer Learning for Speech Introduction to transfer learning for speech, fine-tuning pre-trained models, and domain adaptation
Module #16 Speech Processing for Health Applications Introduction to speech processing for health applications, disease diagnosis, and health monitoring
Module #17 Speech Processing for Robotics and IoT Introduction to speech processing for robotics and IoT, voice control, and human-robot interaction
Module #18 Ethical Considerations in Speech Processing Introduction to ethical considerations in speech processing, bias, and fairness
Module #19 Speech Datasets and Evaluation Metrics Introduction to speech datasets, evaluation metrics, and performance measures
Module #20 Open-Source Toolkits for Speech Processing Introduction to open-source toolkits for speech processing, such as Kaldi, TensorFlow, and PyTorch
Module #21 Real-World Applications of Speech Processing Case studies of real-world applications of speech processing, such as voice assistants, call centers, and voice control
Module #22 Advanced Topics in Speech Processing Introduction to advanced topics in speech processing, such as multichannel signal processing and speech separation
Module #23 Research Directions in Speech Processing Introduction to research directions in speech processing, such as Explainable AI and Multimodal Learning
Module #24 Course Wrap-Up & Conclusion Planning next steps in Machine Learning for Speech Processing career
