Module #1 Introduction to Speech Recognition Overview of speech recognition, its applications, and importance in NLP
Module #2 Fundamentals of Human Speech Understanding the anatomy and physiology of human speech, acoustic characteristics, and speech production
Module #3 Introduction to Natural Language Processing (NLP) Overview of NLP, its subfields, and applications in text and speech processing
Module #4 Speech Recognition Basics Introduction to speech recognition systems, types of speech recognition, and evaluation metrics
Module #5 Acoustic Features for Speech Recognition Extracting acoustic features from speech signals, including Mel-Frequency Cepstral Coefficients (MFCCs)
Module #6 Speech Signal Processing Pre-processing techniques for speech signals, including noise reduction and filtering
Module #7 Deep Learning for Speech Recognition Introduction to deep learning techniques for speech recognition, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)
Module #8 Convolutional Neural Networks (CNNs) for Speech Recognition Applying CNNs to speech recognition, including architectures and performance evaluation
Module #9 Recurrent Neural Networks (RNNs) for Speech Recognition Applying RNNs to speech recognition, including LSTM and GRU architectures
Module #10 Hidden Markov Models (HMMs) for Speech Recognition Introduction to HMMs, including basic concepts and applications in speech recognition
Module #11 Gaussian Mixture Models (GMMs) for Speech Recognition Introduction to GMMs, including basic concepts and applications in speech recognition
Module #12 Language Modeling for Speech Recognition Introduction to language models, including n-gram models and neural network-based language models
Module #13 Decoder Algorithms for Speech Recognition Introduction to decoder algorithms, including Viterbi and beam search algorithms
Module #14 Speech Recognition Systems Overview of speech recognition systems, including commercial and open-source systems
Module #15 Speaker Recognition and Diarization Introduction to speaker recognition and diarization, including algorithms and applications
Module #16 Emotion and Sentiment Analysis from Speech Introduction to emotion and sentiment analysis from speech, including machine learning approaches
Module #17 Spoken Language Understanding (SLU) Introduction to SLU, including intent detection and slot filling
Module #18 Dialogue Systems and Conversational AI Introduction to dialogue systems and conversational AI, including chatbots and virtual assistants
Module #19 Speech Recognition in Noisy Environments Techniques for robust speech recognition in noisy environments, including noise robustness and adaptation
Module #20 Multilingual Speech Recognition Challenges and approaches for multilingual speech recognition, including language adaptation and code-switching
Module #21 Speech Recognition for Specific Domains Speech recognition for specific domains, including medical, financial, and legal applications
Module #22 Ethical Considerations in Speech Recognition Ethical considerations in speech recognition, including privacy, bias, and fairness
Module #23 Evaluation Metrics for Speech Recognition Evaluation metrics for speech recognition, including word error rate, accuracy, and F1 score
Module #24 Advanced Topics in Speech Recognition Advanced topics in speech recognition, including end-to-end speech recognition and transfer learning
Module #25 Course Wrap-Up & Conclusion Planning next steps in Speech Recognition and NLP career
