Introduction to Speech Recognition
Chapter 1: The Foundations of Speech Recognition
What is Automatic Speech Recognition (ASR)?
A Brief History of ASR Systems
The Components of a Speech Recognition Pipeline
Types of Speech Recognition: Speaker-Dependent vs. Speaker-Independent
Types of Speech Recognition: Isolated Word vs. Continuous Speech
How Computers Process Sound: Digital Audio Basics
Introduction to Phonemes and the Building Blocks of Speech
Chapter 2: Processing Audio Signals
From Sound Waves to Digital Data: Sampling and Quantization
Understanding Audio Formats (WAV, MP3, FLAC)
Visualizing Speech: Waveforms and Spectrograms
Pre-emphasis and Framing
Windowing Functions Explained
Introduction to Feature Extraction
Creating Mel-Frequency Cepstral Coefficients (MFCCs)
Hands-on Practical: Visualizing and Processing Audio Files
Chapter 3: Acoustic Modeling
What is an Acoustic Model?
Mapping Sounds to Phonemes
Early Approaches: Gaussian Mixture Models (GMMs)
Hidden Markov Models (HMMs) for Sequential Data
Combining GMMs and HMMs
Introduction to Neural Network-based Acoustic Models
The Role of an Acoustic Model in an ASR System
Chapter 4: Language Modeling
What is a Language Model?
The Problem of Ambiguity in Speech
N-gram Language Models: Bigrams and Trigrams
Calculating Probabilities of Word Sequences
The Concept of Perplexity
How Language Models Improve Accuracy
Introduction to Neural Network Language Models
Chapter 5: Decoding and Putting It All Together
The Role of the Decoder
Finding the Most Likely Sequence of Words
Introduction to Search Algorithms
Understanding the Viterbi Algorithm
The Complete ASR Pipeline: A Review
Evaluating Performance: Word Error Rate (WER)
Common Challenges in Speech Recognition
Chapter 6: Building Your First Speech Recognition Application
Introduction to Speech Recognition APIs and Libraries
Setting Up Your Python Environment
Using a Pre-trained Model for Transcription
Transcribing Audio from a File
Capturing and Transcribing Microphone Input in Real-Time
Handling API Responses and Errors
Practice: Build a Simple Voice Command Tool
The Role of an Acoustic Model in an ASR System
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- Speech and Language Processing, Daniel Jurafsky and James H. Martin, 2025 (Pearson) - Comprehensive textbook covering the principles of ASR, including feature extraction, acoustic modeling (HMM/GMM and deep learning), language modeling, and decoding strategies.
- Deep Neural Networks for Acoustic Modeling in Speech Recognition, Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N. Sainath, and Brian Kingsbury, 2012 IEEE Signal Processing Magazine, Vol. 29 (IEEE) DOI: 10.1109/MSP.2012.2205597 - A seminal paper that details the application and effectiveness of deep neural networks in acoustic modeling, marking a major advancement in ASR.
- An Introduction to Hidden Markov Models, Lawrence R. Rabiner, 1986 IEEE ASSP Magazine, Vol. 3 (IEEE) DOI: 10.1109/MASSP.1986.1165342 - Foundational paper providing a clear exposition of Hidden Markov Models, which form the basis for traditional acoustic modeling in speech recognition systems.