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
Evaluating Performance: Word Error Rate (WER)
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- Speech and Language Processing (3rd Edition Draft), Daniel Jurafsky and James H. Martin, 2023 - The standard textbook for natural language processing and speech, providing a comprehensive and detailed explanation of speech recognition evaluation, including the definition, calculation, and nuances of Word Error Rate (WER).
- Automatic Speech Recognition: A Deep Learning Approach, Dong Yu, Li Deng, 2014 (Springer) DOI: 10.1007/978-1-4471-5779-3 - A foundational book on modern automatic speech recognition, discussing WER as a primary metric for evaluating ASR system performance in the context of deep learning architectures.
- NIST Speech Recognition Scoring Toolkit (SCTK) Documentation, National Institute of Standards and Technology (NIST), 2021 (National Institute of Standards and Technology (NIST)) - Provides the authoritative methodology and tools (like sclite) for calculating Word Error Rate (WER) in speech recognition evaluations, outlining standard practices for alignment, error counting, and normalization.