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
Visualizing Speech: Waveforms and Spectrograms
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- Theory and Application of Digital Speech Processing, Lawrence R. Rabiner, Ronald W. Schafer, 2011 (Pearson) - A foundational textbook dedicated to the digital signal processing of speech. It offers in-depth discussions on time-domain and frequency-domain analysis of speech signals, essential for understanding waveforms and spectrograms.
- Discrete-Time Signal Processing, Alan V. Oppenheim, Ronald W. Schafer, and John R. Buck, 1999 (Pearson) - A classic textbook on digital signal processing that provides the theoretical framework for topics like sampling, Fourier transforms, and spectral analysis, which are fundamental to the generation and interpretation of spectrograms.
- 6.007 Digital Signal Processing, Spring 2011, Alan V. Oppenheim, 2011 (MIT OpenCourseWare) - An accessible university course offering lecture notes and assignments on the fundamentals of digital signal processing, including concepts such as the Fast Fourier Transform and spectral analysis, which are directly applied in creating spectrograms.