Raw Sound as Building Blocks: Next-Gen AI Speech Creation

Question

By Muhammed Shafin P
Licensed under CC BY-SA 4.0

Introduction

When we think about text-to-speech (TTS) technology today, we usually think of systems that take text and produce speech directly. But these systems often sound too robotic or too perfect, and they give you very little control over how the voice behaves.

My concept takes a completely different approach, Instead of focusing on words as the basic unit, it starts from raw sounds, tones, phonemes, and emotional variations, and uses them as building blocks to manually construct speech.

This approach allows full control over every tiny detail of how speech sounds, and it can eventually work for any language or word, even ones that were never recorded before.

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Stage 1: Building the Raw Sound Library

The core of the system is a library of raw sound material,

• These are not words or sentences,
• They are basic sound elements, vowel sounds, consonant sounds, variations of pitch, emotional tones, and frequency-modulated versions,
• Each sound type is tested, adjusted, and labeled, so it can be reused reliably,

Think of it like a paint palette,
You don’t store every possible painting,
You just store all the colors and tools needed to make any painting,

Similarly, this sound library stores all the colors of human sound, happy, sad, sharp, soft, fast, slow, so they can be combined later into any speech.

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Stage 2: Manual Word Building from Blocks

Instead of typing text and getting an automatic result, the user builds words manually using these blocks.

For example, if the target is to create the word “ASAP”:

1. Choose the sound block for “A” from the library,
2. Adjust its controls, pitch, length, emotion, tone quality,
3. Generate the sound for “A” using AI synthesis based on those settings,
4. Choose the block for “SAP,” adjust its settings, and generate that too,
5. If needed, add an extra vowel (like a soft “E” sound) to make the result more natural,
6. Combine these generated parts together to form the full word,

This way, users have studio-like control over how every syllable sounds, but they don’t need to manually record anything.

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AI’s Role: Smart Sound Generation

AI is not used here to directly generate entire phrases,
Instead, AI is used as a precision tool to generate sounds from the chosen building blocks and settings,

For example:

• If the user picks “A” + sad tone + 1.2 second length, AI produces exactly that version of “A”
• If the user picks “P” with a high-pitch energetic tone, AI generates that

This makes AI a sound synthesizer, not a full speech engine.

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The Software Marketplace

The platform will also include a sound marketplace where creators and sound designers can:

• Contribute new raw sound blocks, emotional variants, or frequency-modulated samples,
• Have them verified for quality and added to the shared library,
• Make them available to users who want a larger variety of sound options,

This allows the system to constantly grow with new emotional styles, new voices, and new sound textures, making it more flexible over time.

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Advantages of This Approach

• Infinite Vocabulary: Since speech is built from basic sounds, any word or language can be generated, no need to record entire dictionaries
• Total Control: Users can control pitch, length, speed, emotion, and intensity for each part of speech
• Natural Sounding: By adding small extra sounds (like soft vowels, breaths, or transitions), the result feels realistic and human
• Future-Proof: As AI improves, this process can become semi-automated, letting AI suggest the right blocks and settings, but still allowing manual fine-tuning

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A Practical Example

Let’s say we want to create:

“ASAP, please!” in a worried tone.

Steps might look like this:

• Generate “A” from the sound library with worried emotional settings,
• Generate “SAP” with slightly faster timing to make it sound urgent,
• Add a soft “E” sound between A and SAP for smoother flow,
• Generate “please” with the same emotional settings,
• Combine them in sequence to make the full phrase,

The result: a natural, expressive phrase that feels like a human spoke it, but created entirely from synthetic sound blocks.

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The Future Vision

In the future, this process can be partly or fully automated,
AI could suggest the right blocks, apply emotional settings automatically, and generate entire phrases while still letting users tweak details.

This could revolutionize:

• Voice acting: Generate perfectly tuned lines for movies or games
• Virtual assistants: Give them personality and emotion that feels alive
• Accessibility tools: Allow people to construct speech exactly as they want it to sound
• Music and art: Treat voice as an instrument, with complete freedom over tone and style

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Conclusion

This concept is about giving creators raw sound material and powerful AI tools to construct speech exactly how they imagine it, manually now, automatically in the future.

Instead of AI doing everything in a black box, this system lets users be part of the creative process, selecting, controlling, and fine-tuning every sound until it feels just right.

It’s not just another TTS system, it’s a new way to think about speech generation:
Manual assembly of AI-generated building blocks, powered by a growing library of verified raw sounds.

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This is a concept by Muhammed Shafin P.
Licensed under CC BY-SA 4.0

Comments

Nice idea Muhammed
I think for text-to-speech, this approach suits the south and central asian scripts more where the pronunciation of words is mostly done be concatenating the vowels and consonants involved in the spelling of the words. This may not be so suitable for languages like english where the pronunciations is not always a concatenation of the vowels and consonants found in the word.
You will probably also need a mapping for the letter and letter combinations with the phoneme.
But really good to use AI for adding emotions.

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sir basically this can work on english.i got it what you meant.this base or building blocks isnt letters.so its possible to build upon it.so 'Grapheme-to-phoneme conversion' is must be manually converted - that is also a cause of manual beacuse ai cant make it correctly or choose it to build from blocks.

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you thing it have bug.but basically its a feature because most of it cant be now executed by ai correctly.

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as you said mapping or automated mapping can be added as custom plugin or something like that,but the base must be very base so every thing can be builded from it.this carries fundmental level.

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i created that sample ,so everyone who reads can understand.but if you read carefully you can get how much deeper or where it points.sample construction can be a simple one.but there is even an option to create a new letter(phoneme) itself bcz its uses basic things.so no worries.for get cleared, please read again.

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Step-by-Step Process:

1. Start with Base Acoustic Properties

Frequency range: Set fundamental frequency (e.g., 200-400 Hz)
Harmonic structure: Choose overtone patterns
Duration: Set length (e.g., 0.3 seconds)
Amplitude envelope: How the sound starts/peaks/fades

1. Blend Existing Phoneme Characteristics

Take 60% of an "A" vowel's resonance
Add 30% of an "O" vowel's mouth shape acoustics
Mix in 10% of a "M" consonant's nasal quality
Result: A completely new vowel-like sound

1. Add Unique Modulations

Pitch bend: Slight rising tone throughout
Throat tension: Different from any natural phoneme
Airflow pattern: Unique breathing characteristic
Emotional coloring: Built-in "curiosity" tone

1. Fine-tune with AI Synthesis
   The AI would generate this sound based on your specifications, then you could:

Adjust the blend percentages
Modify the acoustic properties
Test how it sounds in combination with other phonemes

1. Save as New Building Block
   Once perfected, this new phoneme gets added to your sound library for reuse.
   Example Result: You might create a sound that's like saying "Aum" but with a built-in questioning inflection and slight nasal quality - something that doesn't exist in any human language but sounds natural and expressive.

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consider this as theoractical sample procedure.

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Hello Muhammed,
You talked about capturing vowel and consonant sounds.
One thing you ought to know that consonants cannot be capture independently, they always have to have a vowel along with them for a sound.
A pure consonant is never captured.

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thats why i said its basic means highly basic,raw sounds are used as building blocks ,whether phenome or not you can build as you want - some phenomes like english will be already builded as in library as ready to use,else u can build it from scratch uisng changing wave's properties and which type you want with help of ai synthesis - its become more time consuming(thats only drawback) because may be wants edit in millisecond level

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i think it mentioned 'consontant sounds',basically it is a building block not said that it is recorded.Building blocks all arent recorded sounds.it contains more than that data on waves and what others needs to generate that sounds,which ai are used with building blocks so it helps based on the of materials and data which is inside it.some building blocks are also crafted not recorded.recorded are different from created which is from the recorded sounds by stitching os many things.also some is built using analyzing the recorded sounds then try to recreate,then after study try to craete a new letter based on that person sound(recorded person),so now fully newly phenomes and all others can be craeted based on that peron's sound. My typing makes some spelling mistakes,but i think you can understand it.for further,you may be needed to learn about sounds more deeply.