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
Capturing and Transcribing Microphone Input in Real-Time
Was this section helpful?
- SpeechRecognition Documentation, Anthony Zhang, 2023 - Provides comprehensive details on the library's API, including microphone handling, recognition methods, and noise adjustment, essential for practical implementation.
- Speech and Language Processing, Daniel Jurafsky and James H. Martin, 2025 (Stanford University) - A comprehensive textbook covering the theoretical foundations and practical aspects of automatic speech recognition, including audio processing and real-time considerations.
- Digital Speech Processing: Fundamentals and Applications, Lawrence Rabiner and Ronald W. Schafer, 2011 (Pearson) - Offers in-depth coverage of speech signal processing techniques, including methods for noise reduction and feature extraction relevant to robust speech recognition in real-world environments.
- Hands-On Speech Recognition with Python, Michael F. S. Chen, 2019 (Packt Publishing) - A practical guide demonstrating how to build speech recognition applications using Python, covering libraries like SpeechRecognition and addressing real-world challenges such as live audio processing and noise management.