Speech Recognition and Conversational AI
web-development.
Course Modules:
Module 1: Introduction to Speech and Conversational AI
What is ASR and conversational AI?
Evolution of voice technology and virtual assistants
Use cases in business, healthcare, education, and smart devices
Module 2: Fundamentals of Speech Recognition
Audio signals and speech features (MFCC, spectrograms)
Preprocessing voice data
Speech-to-text systems and decoding techniques
Module 3: NLP for Conversational Systems
Intent recognition and entity extraction
Tokenization, stemming, and named entity recognition (NER)
Understanding context in dialogues
Module 4: Text-to-Speech (TTS) and Voice Synthesis
Converting text to natural-sounding speech
Tools: Tacotron, WaveNet, pyttsx3, Google TTS
Customizing voice, tone, and language
Module 5: Chatbot Design & Dialogue Management
Rule-based vs. AI-powered chatbots
Building multi-turn conversations
Dialog state tracking and context retention
Module 6: Tools and Platforms for Voice AI
Open-source libraries: DeepSpeech, Whisper, SpeechBrain
Rasa, Dialogflow, Amazon Lex, Microsoft Bot Framework
Integrating APIs and SDKs into applications
Module 7: Real-Time Speech Applications
Streaming audio processing
Real-time transcription and translation
Voice commands for smart devices
Module 8: Privacy, Security & Ethical Challenges
Voice data protection and GDPR compliance
Preventing voice spoofing and impersonation
Inclusivity and accent diversity in training models
Module 9: Multimodal and Adaptive Interaction
Combining voice with text, vision, and touch
Adaptive UI and personalization
Context-aware conversation agents
Module 10: Capstone Project
Build a real-world application such as:
A customer service voicebot
A hands-free AI tutor
A multilingual voice-enabled interface
Submit full project with demo, transcript logs, and UX overview
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