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

WIZAPE

ONLINE RESOURCES

ABOUT WIZAPE

ONLINE PORTAL

Our priority is to cultivate a vibrant community before considering the release of a token. By focusing on engagement and support, we can create a solid foundation for sustainable growth. Let’s build this together!

We're giving our website a fresh new look and feel! 🎉 Stay tuned as we work behind the scenes to enhance your experience.
Get ready for a revamped site that’s sleeker, and packed with new features. Thank you for your patience. Great things are coming!

CONTACT-US PRIVACY POLICY

Natural Language Processing for Speech Recognition ( 25 Modules )

Natural Language Processing for Speech Recognition
( 25 Modules )