TL;DR
Batch leaderboards are misleading for real-world use. Latency wins
Integrated voice APIs (speech → thinking → speech) outperform stitched systems
The shift from phonemes to neural voice is not incremental. It’s architectural
Every business will end up with a voice interface. Sooner than you think
The shift from phonemes to neural voice is not incremental. It’s architectural
Every business will end up with a voice interface. Sooner than you think
I Built a Talking Robot in 1989…
And I had to program it using phonemes.
Not words. Not sentences. Just sound fragments.
I remember typing hex codes to make a Heathkit HERO Jr. robot sing a college fight song.
It worked. Kind of.
But it was robotic, rigid, and took hours to get even a single sentence right.
Fast forward to today.
You speak. AI listens. It responds instantly in a natural voice.
That jump is not just progress.
It’s a complete shift in how audio AI is built.
What Actually Changed
At a high level, nothing changed.
We still have:
- Speech → Text
- Text → Speech
But under the hood?
Everything changed.
Speech-to-Text: Good Enough Is No Longer Enough
Modern systems like Whisper and Google Gemini have pushed accuracy to very high levels.
But here’s what most people miss:
👉 Accuracy is no longer the bottleneck
👉 Latency is
👉 Latency is
Batch leaders like ElevenLabs Scribe or Gemini variants look great on paper.
But for real-world use like voice chat?
Models like Deepgram Nova or streaming Gemini matter more.
Because if the system pauses, hesitates, or corrects itself mid-sentence…
The experience breaks.
Text-to-Speech: From Robotic to Indistinguishable
Old systems stitched sounds together.
Today’s systems generate voice.
Models like VALL-E treat audio like language itself.
That’s why:
- You get emotion
- You get tone
- You can clone voices
And the best part?
You don’t think about phonemes anymore.
The system does it for you.
Batch vs Real-Time: The Mistake Everyone Makes
This is where most teams go wrong.
They pick tools based on leaderboard rankings.
But those rankings are usually batch-based.
Which means:
- High quality
- No time constraints
But real users?
They want:
- Instant response
- Interruptions
- Flow
👉 For voice AI, real-time performance beats raw quality every time
The Big Shift: Integrated Voice AI
Traditional architecture looked like this:
Speech → STT → LLM → TTS → Audio
Each step adds a delay.
Each step introduces friction.
Now?
Systems like OpenAI Realtime API and Google Gemini Live are collapsing everything into one loop:
👉 Speech → Thinking → Speech
No intermediate steps.
Why this wins:
- Faster responses
- More natural conversations
- Better interruption handling
- Cleaner architecture
And honestly…
Even if you pick the best individual components…
It still feels worse than an integrated system.
So, What Should You Do?
If you’re building anything with voice:
- Stop optimizing for accuracy alone
- Start optimizing for experience
- Prioritize real-time APIs over stitched pipelines
Because voice is not just output anymore.
It’s the interface.
Where This Is Going
Every business will end up with:
- A voice layer
- A knowledge-backed AI
- A real-time conversational interface
Not chatbots.
Voice agents.
Want the Full Breakdown?
I went much deeper into:
- Full STT pipeline
- Full TTS pipeline
- Top models and leaderboards
- Real-time vs batch comparison
- Architecture decisions for voice AI
👉 Read the full blog here: From Phonemes to Real-Time Voice AI
If you’re thinking about adding voice to your product or business, this is one of those moments where being early actually matters.
Because once users get used to talking…
They don’t go back to typing.
Thanks for reading Signal Over Noise,
where we separate real business signal from AI noise.
where we separate real business signal from AI noise.
See you next Tuesday,
Avi Kumar
Founder: Kuware.com
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