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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

Chapter 1: The Foundations of Speech Recognition

This chapter provides a high-level overview of Automatic Speech Recognition (ASR), establishing the core terminology and the structure of a typical system. We start by defining ASR, looking at its historical development, and outlining the key components that work together to turn speech into text.

By working through this material, you will learn to:

Define what Automatic Speech Recognition is and identify its primary function.
Describe the main stages of an ASR pipeline, from signal processing to the final decoded output.
Distinguish between different types of ASR systems, such as those built for a specific speaker versus any speaker.
Explain the fundamentals of how sound is represented digitally and the role of phonemes in language.

Sections

1.1 What is Automatic Speech Recognition (ASR)?
1.2 A Brief History of ASR Systems
1.3 The Components of a Speech Recognition Pipeline
1.4 Types of Speech Recognition: Speaker-Dependent vs. Speaker-Independent
1.5 Types of Speech Recognition: Isolated Word vs. Continuous Speech
1.6 How Computers Process Sound: Digital Audio Basics
1.7 Introduction to Phonemes and the Building Blocks of Speech

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