Introduction to Speech Synthesis (Text-to-Speech)

Until now, this course focused on understanding spoken language through Automatic Speech Recognition (ASR).

Speech synthesis, also called Text-to-Speech (TTS), solves the opposite problem:

How do we convert text into natural-sounding speech?

This lesson introduces the core ideas, history, and engineering foundations behind modern speech synthesis systems.

What Is Speech Synthesis?

Speech synthesis is the process of generating human-like speech from text.

A TTS system takes text such as:

"Welcome to the Speech AI course."

and produces an audio waveform that sounds natural and intelligible.

Why Speech Synthesis Matters

Speech synthesis is critical in modern applications:

• Voice assistants
• Accessibility tools
• Audiobooks
• Customer service bots

Good TTS improves user trust and experience. Poor TTS breaks immersion instantly.

High-Level TTS Pipeline

Before diving into models, it’s important to understand the pipeline.

A typical TTS system follows this flow:

Text → Linguistic Processing → Acoustic Modeling → Vocoder → Audio

Each stage solves a different problem.

Step 1: Text Processing

Raw text is not directly suitable for speech generation.

Text processing handles:

• Text normalization
• Number expansion
• Abbreviations
• Punctuation

Why This Code Exists

This code demonstrates text normalization, a mandatory first step in TTS systems.

def normalize_text(text):
    text = text.lower()
    text = text.replace("AI", "artificial intelligence")
    return text

normalized = normalize_text("AI is changing the world.")
print(normalized)
  

What happens here:

• Text is standardized
• Abbreviations are expanded

Why this matters:

If text is not normalized, the speech output will sound unnatural or incorrect.

Step 2: Linguistic Representation

TTS systems must understand how words should be pronounced.

This involves converting text into:

• Phonemes
• Stress patterns
• Syllable boundaries

Why This Code Exists

This code shows how text can be mapped to phonemes using a pronunciation dictionary.

phoneme_dict = {
  "hello": ["HH", "AH", "L", "OW"],
  "world": ["W", "ER", "L", "D"]
}

print(phoneme_dict["hello"])
  

What this does:

• Maps words to sounds
• Removes ambiguity in pronunciation

Why this improves TTS:

Correct pronunciation is essential for intelligibility.

Step 3: Acoustic Modeling

Acoustic models predict how speech should sound.

They map linguistic features to:

• Pitch
• Duration
• Spectral features

Modern systems use deep learning for this stage.

Why This Code Exists

This pseudocode represents how an acoustic model generates features.

mel_features = acoustic_model(phoneme_sequence)
print(mel_features.shape)
  

What happens here:

• Linguistic inputs become acoustic features
• Timing and intonation are learned

Why this matters:

This step controls how natural the voice sounds.

Step 4: Vocoder

The vocoder converts acoustic features into raw audio waveforms.

This is where speech becomes audible.

Why This Code Exists

This code shows how a vocoder generates speech from Mel spectrograms.

audio_waveform = vocoder(mel_features)
play(audio_waveform)
  

What this does:

• Transforms features into sound
• Produces human-like speech

Why the vocoder is critical:

Most quality improvements in modern TTS come from better vocoders.

Evolution of TTS Systems

Speech synthesis has evolved significantly:

• Rule-based synthesis
• Concatenative synthesis
• Statistical parametric synthesis
• Neural TTS

Modern systems are fully neural.

Challenges in Speech Synthesis

Building high-quality TTS is hard due to:

• Prosody modeling
• Emotion control
• Speaker consistency

Later lessons will address these challenges.

Practice

Quick Quiz

Recap: Speech synthesis converts text into speech using text processing, acoustic modeling, and vocoding.

Next up: You’ll learn the fundamentals of Text-to-Speech models and how neural TTS systems are trained.