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
