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
