Master Study AI

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

 

🧠Master Study NLP Fundamentals: The Foundation of Language Understanding in AI

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