Natural Language Processing for Speech Recognition
( 25 Modules )
Module #1 Introduction to Speech Recognition Overview of speech recognition, its applications, and importance of NLP in speech recognition
Module #2 Basics of Natural Language Processing Introduction to NLP, its subfields, and key concepts (tokenization, stemming, lemmatization, etc.)
Module #3 Speech Signal Processing Overview of speech signal processing, acoustic features, and feature extraction techniques
Module #4 Phonetics and Phonology Introduction to phonetics and phonology, phonemes, and phoneme recognition
Module #5 Statistical Models for Speech Recognition Introduction to statistical models (Bayes theorem, probability theory, and conditional probability)
Module #6 Hidden Markov Models (HMMs) Introduction to HMMs, their architecture, and applications in speech recognition
Module #7 HMMs for Speech Recognition Using HMMs for speech recognition, including Gaussian mixture models and ergodic HMMs
Module #8 N-Grams and Language Modeling Introduction to N-grams, language modeling, and their applications in speech recognition
Module #9 Language Model Architectures Introduction to language model architectures, including feedforward and recurrent neural networks
Module #10 Deep Learning for Speech Recognition Introduction to deep learning, including convolutional neural networks and recurrent neural networks
Module #11 Convolutional Neural Networks (CNNs) for Speech Recognition Using CNNs for speech recognition, including spectrogram-based and filterbank-based approaches
Module #12 Recurrent Neural Networks (RNNs) for Speech Recognition Using RNNs for speech recognition, including LSTMs and GRUs
Module #13 Attention Mechanisms in Speech Recognition Introduction to attention mechanisms and their applications in speech recognition
Module #14 End-to-End Speech Recognition Introduction to end-to-end speech recognition, including sequence-to-sequence models and CTC loss
Module #15 Acoustic Modeling Introduction to acoustic modeling, including acoustic feature extraction and acoustic scoring
Module #16 Language Model Adaptation Introduction to language model adaptation, including domain adaptation and speaker adaptation
Module #17 Error Correction and Post-processing Introduction to error correction and post-processing techniques, including confidence scoring and lattice pruning
Module #18 Speech Recognition Systems Overview of speech recognition systems, including system architecture and component integration
Module #19 Speech Recognition Applications Applications of speech recognition, including voice assistants, voice control, and transcription systems
Module #20 Challenges in Speech Recognition Challenges in speech recognition, including noise robustness, speaker variability, and language complexity
Module #21 Evaluation Metrics for Speech Recognition Evaluation metrics for speech recognition, including WER, CER, and NIST scores
Module #22 Speech Recognition Toolkits and Software Overview of speech recognition toolkits and software, including Kaldi, OpenCV, and Mozilla DeepSpeech
Module #23 NLP for Speech Recognition Case Study Real-world case study of using NLP for speech recognition in a specific domain or application
Module #24 Future Directions in Speech Recognition Future directions in speech recognition, including emerging trends and areas of research
Module #25 Course Wrap-Up & Conclusion Planning next steps in Natural Language Processing for Speech Recognition career
