Module #1 Introduction to Speech Recognition Overview of speech recognition, its applications, and the importance of advanced techniques.
Module #2 Acoustic Modeling Fundamentals Review of acoustic modeling basics, including Gaussian mixture models and hidden Markov models.
Module #3 Deep Neural Networks for Acoustic Modeling Introduction to using deep neural networks for acoustic modeling, including convolutional and recurrent neural networks.
Module #4 Transfer Learning in Speech Recognition Exploring the use of transfer learning to adapt pre-trained models for speech recognition tasks.
Module #5 Language Modeling Fundamentals Review of language modeling basics, including n-gram models and statistical language models.
Module #6 Neural Language Models for Speech Recognition Introduction to using neural networks for language modeling, including recurrent and transformer-based models.
Module #7 Integration of Acoustic and Language Models Exploring techniques for integrating acoustic and language models for speech recognition.
Module #8 Advanced Feature Extraction Techniques Exploring advanced feature extraction techniques, including filterbanks and spectrograms.
Module #9 Signal Processing for Robust Speech Recognition Techniques for robust speech recognition, including noise reduction and echo cancellation.
Module #10 Speaker Adaptation and Normalization Exploring techniques for speaker adaptation and normalization, including vocal tract length normalization.
Module #11 Multichannel Speech Recognition Exploring techniques for multichannel speech recognition, including beamforming and microphone arrays.
Module #12 Keyword Spotting and Speech Tagging Introduction to keyword spotting and speech tagging, including techniques for detecting specific words or phrases.
Module #13 Emotion and Sentiment Analysis in Speech Exploring techniques for emotion and sentiment analysis in speech, including acoustic and lexical features.
Module #14 Speaker Diarization and Identification Introduction to speaker diarization and identification, including techniques for tracking speaker turns and identifying speakers.
Module #15 Conversational Speech Recognition Exploring challenges and techniques for conversational speech recognition, including dialogue management and turn-taking.
Module #16 Speech Recognition for Low-Resource Languages Exploring techniques for speech recognition in low-resource languages, including data augmentation and transfer learning.
Module #17 End-to-End Speech Recognition Models Introduction to end-to-end speech recognition models, including sequence-to-sequence and attention-based models.
Module #18 Attention Mechanisms in Speech Recognition Exploring the role of attention mechanisms in speech recognition, including attention-based sequence-to-sequence models.
Module #19 Domain Adaptation and Robustness in Speech Recognition Techniques for domain adaptation and robustness in speech recognition, including domain invariant feature extraction.
Module #20 Explainability and Interpretability in Speech Recognition Exploring techniques for explainability and interpretability in speech recognition, including model interpretability and feature importance.
Module #21 Adversarial Attacks and Defense in Speech Recognition Exploring adversarial attacks and defense techniques in speech recognition, including robustness to audio adversaries.
Module #22 Speech Enhancement and Separation Techniques for speech enhancement and separation, including speech dereverberation and speech separation.
Module #23 Multimodal Speech Recognition Exploring techniques for multimodal speech recognition, including fusion of audio and visual cues.
Module #24 Real-World Applications of Advanced Speech Recognition Case studies of advanced speech recognition in real-world applications, including virtual assistants and speech-to-text systems.
Module #25 Course Wrap-Up & Conclusion Planning next steps in Advanced Speech Recognition Techniques career
