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
Introduction to Neural Network-based Acoustic Models
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- 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, Brian Kingsbury, 2012 IEEE Signal Processing Magazine, Vol. 29 (IEEE) DOI: 10.1109/MSP.2012.2205597 - Foundational paper that demonstrated the effectiveness of replacing Gaussian Mixture Models with Deep Neural Networks for acoustic modeling in hybrid HMM-DNN speech recognition systems.
- Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks, Alex Graves, Santiago Fernández, Faustino Gomez, Jürgen Schmidhuber, 2006 Proceedings of the 23rd International Conference on Machine Learning (ACM) DOI: 10.1145/1143844.1143891 - Introduces Connectionist Temporal Classification (CTC), a method for training recurrent neural networks to label unsegmented sequence data, widely used in end-to-end ASR.
- Listen, Attend and Spell: A Neural Network for Large Vocabulary Conversational Speech Recognition, William Chan, Navdeep Jaitly, Quoc V. Le, Oriol Vinyals, 2016 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE) DOI: 10.1109/ICASSP.2016.7472659 - Presents the 'Listen, Attend and Spell' (LAS) model, an early and influential example of an attention-based encoder-decoder architecture for end-to-end speech recognition.