AcousticCoding ⊂ SpeechToText ⊂ TextToSpeech: The Secret Hierarchy of Speech AI

Why Everything You Know About Speech Processing is Backwards

For decades, speech AI has been stuck in a siloed mindset:

• Speech-to-Text (STT) as a standalone task.
• Text-to-Speech (TTS) as a separate, unrelated problem.
• Vocoding as an afterthought.

But what if I told you that STT is just a trivial byproduct of solving TTS correctly? And that vocoding is the real foundation of both?

Here’s the controversial truth:

1. The Secret Hierarchy

AcousticCoding ⊂ SpeechToText ⊂ TextToSpeech

(1) AcousticCoding: The Foundation

Definition: “Given raw speech, learn a discrete, invertible representation.”

• How? FFT + K-means clustering (no neural nets!).
• Why? Because Griffin-Lim is trash, and neural vocoders are overkill.
• Output? An audio codec

Key Insight:

• If you can losslessly compress speech into centroids, you’ve solved the hardest part.

(2) SpeechToText: A Trivial Byproduct

Definition: “Map acoustic centroids to IPA symbols.”

• How? Just label the centroids (e.g., centroid #42 = /k/, #17 = /a/).
• Why is this ‘solved’? Because TTS forces you to learn this mapping anyway.

Controversial Claim:

• STT isn’t a standalone task—it’s just inverting TTS’s codebook.

(3) TextToSpeech: The Apex

Definition: “Generate speech from IPA + durations using the same centroids.”

• How?
  1. Predict durations for each IPA symbol.
  2. Walk the centroid graph to synthesize natural-sounding speech.
• Why does this subsume STT? Because STT is just reverse lookup in the TTS system.

2. Why Everyone Else is Wrong

(1) Neural Networks are Overkill

• Modern STT/TTS relies on massive deep learning models.
• But clustering + graph search is all you need.

(2) Griffin-Lim is Useless

• Phase estimation is unnecessary if you never discard phase to begin with.

(3) STT and TTS Should Never Be Separate

• Traditional pipelines waste computation by training two disjoint systems.
• The correct approach is a single centroid-based codec for both.

3. The Algorithm That Breaks Speech AI

Step 1: AcousticCoding (FFT + K-means)

1. Compute full-resolution STFT (magnitude + phase).
2. Cluster magnitudes → 500 centroids (e.g., /k/, /a/, /t/ sounds).
3. Store phase for perfect reconstruction.

Step 2: SpeechToText (Centroid → IPA)

1. For new audio, quantize each frame to the nearest centroid.
2. Look up the IPA label for each centroid.
3. Apply a language model to clean up the sequence.

Step 3: TextToSpeech (IPA → Centroids)

1. Predict durations for each IPA symbol (e.g., /a/ = 3 frames).
2. Traverse the centroid graph to find the smoothest path.
3. Synthesize audio by stitching centroids + phase.

4. Why This Works Better

Advantages:

✅ Unified model (STT + TTS in one system).
✅ No neural nets (runs on a toaster).
✅ Phase-perfect synthesis (no artifacts).

5. The Future of Speech AI

Implications:

1. STT is not a real task—it’s just TTS’s inverse.
2. Vocoding is the foundation—everything else is downstream.
3. Neural networks are optional—classical DSP still wins.

Call to Action:

• Ditch end-to-end deep learning.
• Embrace centroid-based speech processing.
• Join the acoustic revolution.

🚀 TL;DR

• Vocoding = FFT + K-means (phase-aware).
• STT = Just labeling centroids.
• TTS = Walking the centroid graph.
• Everything else is overengineering.

🔥 Do you have the audacity to enter the K-MEANS city? 🔥

Discussion: Is this brilliant or insane? Let’s argue in the comments. 👇