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 Concept of Perplexity
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- Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, Daniel Jurafsky and James H. Martin, 2025 (Pearson) - A comprehensive textbook offering detailed explanations of language models, N-grams, and perplexity as a metric for evaluation.
- Foundations of Statistical Natural Language Processing, Christopher D. Manning and Hinrich Schütze, 1999 (The MIT Press) - A foundational text in statistical NLP, covering the mathematical underpinnings of language models and the role of perplexity in their assessment.
- Fundamentals of Speech Recognition, Lawrence R. Rabiner, Biing-Hwang Juang, 1993 (Prentice Hall) - A classic textbook in speech recognition, providing context for language model evaluation, including perplexity, within ASR systems.
- One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling, Ciprian Chelba, Thorsten Brants, Anna Chorowski, Michael Kayser, Philip Robinson, and Li Zhang, 2014 International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE) DOI: 10.1109/ICASSP.2014.6854890 - Introduces a widely-used benchmark dataset and evaluates various language models using perplexity, illustrating its practical importance in research.