Speech Recognition and NLP
( 25 Modules )

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

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Speech Recognition and NLP ( 25 Modules )

Speech Recognition and NLP
( 25 Modules )