Speech Recognition and Processing

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Mentor Based

Program Syllabus

Module 1

About This Course

This program covers key concepts in speech signal processing, Automatic Speech Recognition (ASR), and natural language understanding. Participants will explore deep learning models like RNNs and CNNs for speech recognition, voice command systems, and speech synthesis. Additionally, the course includes practical sessions on implementing ASR systems using Python-based libraries.

Module 2

Aim

To provide an advanced understanding of speech recognition systems and signal processing techniques, enabling participants to develop AI-driven solutions for speech-to-text, voice commands, and natural language interfaces. This course focuses on modern algorithms, architectures, and real-world applications.

Module 3

Program Objectives

Understand the principles of speech recognition and signal processing.
Learn how to build, train, and optimize speech-to-text models.
Explore speech synthesis and voice generation techniques.
Gain hands-on experience implementing speech recognition systems.
Understand the challenges and advancements in real-time speech processing.

Module 4

Program Structure

Introduction to Speech Recognition and Processing
- Overview of Speech Recognition and Its Applications
- History and Evolution of Speech Technology
- Challenges in Speech Recognition (Accents, Noise, etc.)
Fundamentals of Speech Signals
- Speech Signal Characteristics
- Time-Domain and Frequency-Domain Representations
- Spectrograms and Waveforms
Signal Preprocessing Techniques
- Digital Signal Processing (DSP) Basics
- Feature Extraction: MFCCs (Mel-Frequency Cepstral Coefficients)
- Spectral Features and Filter Banks
Hidden Markov Models (HMMs) for Speech Recognition
- Introduction to HMMs
- Acoustic Models and Phoneme Recognition
- Decoding with HMMs for Speech Recognition Systems
Deep Learning for Speech Recognition
- Introduction to End-to-End Speech Recognition
- Convolutional Neural Networks (CNNs) in Speech
- Recurrent Neural Networks (RNNs), LSTMs, and GRUs for Sequential Speech Data
Automatic Speech Recognition (ASR) Systems
- ASR Architecture (Acoustic Model, Language Model)
- Speech-to-Text Pipeline (Data Flow from Speech to Recognized Text)
- Popular ASR Systems (e.g., Google ASR, DeepSpeech)
Language Models for Speech Recognition
- Statistical Language Models (n-grams)
- Neural Language Models (Transformers for Speech)
- Integration of Language Models with ASR Systems
Speaker Recognition and Identification
- Voice Biometrics: Speaker Identification and Verification
- Speaker Embeddings (e.g., i-Vectors, x-Vectors)
- Applications in Security and Personalization
Speech Synthesis and Text-to-Speech (TTS)
- Overview of Speech Synthesis
- WaveNet and Tacotron Architectures
- Real-World Applications of TTS (e.g., Voice Assistants)
Speech Enhancement and Noise Reduction
- Techniques for Speech Denoising
- Speech Enhancement with Deep Learning Models
- Real-Time Applications in Call Centers and Assistive Technologies
Ethics and Bias in Speech Technology
- Bias in ASR Systems (Gender, Accent, Dialect Biases)
- Ethical Considerations in Voice Data Collection
- Privacy Issues in Speech-Enabled Systems

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.