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.

Program Structure

Module 1: 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.)

Module 2: Fundamentals of Speech Signals

• Speech Signal Characteristics
• Time-Domain and Frequency-Domain Representations
• Spectrograms and Waveforms

Module 3: Signal Preprocessing Techniques

• Digital Signal Processing (DSP) Basics
• Feature Extraction: MFCCs (Mel-Frequency Cepstral Coefficients)
• Spectral Features and Filter Banks

Module 4: Hidden Markov Models (HMMs) for Speech Recognition

• Introduction to HMMs
• Acoustic Models and Phoneme Recognition
• Decoding with HMMs for Speech Recognition Systems

Module 5: 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

Module 6: 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)

Module 7: Language Models for Speech Recognition

• Statistical Language Models (n-grams)
• Neural Language Models (Transformers for Speech)
• Integration of Language Models with ASR Systems

Module 8: Speaker Recognition and Identification

• Voice Biometrics: Speaker Identification and Verification
• Speaker Embeddings (e.g., i-Vectors, x-Vectors)
• Applications in Security and Personalization

Module 9: Speech Synthesis and Text-to-Speech (TTS)

• Overview of Speech Synthesis
• WaveNet and Tacotron Architectures
• Real-World Applications of TTS (e.g., Voice Assistants)

Module 10: Speech Enhancement and Noise Reduction

• Techniques for Speech Denoising
• Speech Enhancement with Deep Learning Models
• Real-Time Applications in Call Centers and Assistive Technologies

Module 11: 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

Module 12: Final Project

• Building a Speech Recognition System for a Real-World Application (e.g., Command Recognition, Speech-to-Text)