Speech Recognition for Natural Language Processing
( 25 Modules )

Module #1
Introduction to Speech Recognition
Overview of speech recognition, its applications, and importance in NLP

Module #2
History of Speech Recognition
Evolution of speech recognition from early beginnings to modern techniques

Module #3
Acoustic Phonetics
Basic properties of speech sounds, phonemes, and acoustic features

Module #4
Signal Processing for Speech
Introduction to digital signal processing, filtering, and feature extraction

Module #5
Speech Recognition Fundamentals
Basic concepts of speech recognition, including pattern recognition and machine learning

Module #6
Hidden Markov Models (HMMs)
Introduction to HMMs, a fundamental technique in speech recognition

Module #7
Gaussian Mixture Models (GMMs)
GMMs for speech modeling, including parameter estimation and inference

Module #8
Deep Neural Networks (DNNs) for Speech Recognition
Introduction to DNNs, including feedforward and recurrent neural networks

Module #9
Recurrent Neural Networks (RNNs) for Speech Recognition
RNNs and Long Short-Term Memory (LSTM) networks for speech recognition

Module #10
Convolutional Neural Networks (CNNs) for Speech Recognition
CNNs for speech recognition, including spectrogram analysis

Module #11
End-to-End Speech Recognition
Direct speech-to-text models, including sequence-to-sequence and attention-based models

Module #12
Language Models for Speech Recognition
Role of language models in speech recognition, including n-gram and neural language models

Module #13
Decoding and Post-processing
Decoding techniques, including beam search and lattice rescoring

Module #14
Speech Recognition Applications
Real-world applications of speech recognition, including voice assistants and speech-to-text systems

Module #15
Challenges in Speech Recognition
Common challenges, including noise robustness, accents, and limited data

Module #16
Multilingual and Multimodal Speech Recognition
Speech recognition for multiple languages and modalities, including speech and lip movements

Module #17
Evaluation Metrics for Speech Recognition
Metrics for evaluating speech recognition systems, including WER, PER, and BLEU

Module #18
Open-Source Toolkits for Speech Recognition
Introduction to popular open-source toolkits, including Kaldi, TensorFlow, and PyTorch

Module #19
Building a Speech Recognition System
Hands-on exercise building a basic speech recognition system using a selected toolkit

Module #20
Advanced Topics in Speech Recognition
Recent advances in speech recognition, including transfer learning and adversarial training

Module #21
Ethical Considerations in Speech Recognition
Ethical implications of speech recognition, including bias, privacy, and accessibility

Module #22
Case Studies in Speech Recognition
Real-world case studies of speech recognition applications, including voice assistants and speech-to-text systems

Module #23
Future Directions in Speech Recognition
Future research directions, including multispeaker recognition and speech recognition in the wild

Module #24
Project Development and Presentation
Students will work on a project and present their results, applying concepts learned throughout the course

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Speech Recognition for Natural Language Processing career

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Speech Recognition for Natural Language Processing ( 25 Modules )

Speech Recognition for Natural Language Processing
( 25 Modules )