Natural Language Processing in Speech Technology
( 25 Modules )

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

Module #2
Fundamentals of Natural Language Processing
Defining NLP, its subfields, and key concepts

Module #3
Speech Signal Processing
Acoustics of speech, signal processing techniques, and feature extraction

Module #4
Phonetics and Phonology
Study of speech sounds, phonetic transcription, and phonological rules

Module #5
Speech Recognition Basics
Introduction to speech recognition, types of recognition, and acoustic modeling

Module #6
Acoustic Modeling
Types of acoustic models, Gaussian mixture models, and hidden Markov models

Module #7
Language Modeling
N-gram language models, statistical language modeling, and smoothing techniques

Module #8
Decoding and Search Algorithms
Beam search, Viterbi algorithm, and other decoding techniques

Module #9
Speech Recognition Systems
Overview of speech recognition systems, architectures, and applications

Module #10
Deep Learning for Speech Recognition
Introduction to deep learning, CNNs, RNNs, and DNNs for speech recognition

Module #11
Convolutional Neural Networks for Speech
Applying CNNs to speech recognition, speech enhancement, and feature extraction

Module #12
Recurrent Neural Networks for Speech
Applying RNNs to speech recognition, language modeling, and speech synthesis

Module #13
Natural Language Understanding
Introduction to NLU, intent detection, and slot filling

Module #14
Dialogue Systems
Designing conversational agents, dialogue management, and response generation

Module #15
Sentiment Analysis and Emotion Detection
Analyzing sentiment and emotions in speech, techniques, and applications

Module #16
Speaker Recognition and Diarization
Speaker recognition, speaker diarization, and applications

Module #17
Speech Synthesis and Text-to-Speech
Introduction to speech synthesis, TTS systems, and unit selection

Module #18
Deep Learning for Speech Synthesis
Applying deep learning to speech synthesis, WaveNet, and end-to-end TTS

Module #19
Spoken Language Translation
Introduction to spoken language translation, machine translation, and evaluation metrics

Module #20
Speech and Multimodality
Multimodal interaction, fusion of speech and vision, and applications

Module #21
Evaluation Metrics for Speech Technology
Metrics for speech recognition, synthesis, and dialogue systems

Module #22
Ethics and Bias in Speech Technology
Ethical considerations, bias in speech technology, and fairness

Module #23
Real-World Applications of Speech Technology
Case studies of speech technology in various industries and domains

Module #24
Current Trends and Future Directions
Recent advancements, trends, and future directions in speech technology

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

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Natural Language Processing in Speech Technology ( 25 Modules )

Natural Language Processing in Speech Technology
( 25 Modules )