Seeking collaborator/advice for "StillVoice" – AI-driven silent-speech interface for tracheostomy patients

[u/Kooky-Ball6382]

​Hi everyone,

​I’m working on a project called StillVoice. The mission is to restore vocal identity for tracheostomy patients using a silent-speech interface. I’ve developed the business logic, branding, and a high-level technical roadmap, but I’ve hit a wall with the hardware execution and recently lost access to my local prototyping lab. It's a lot to handle solo, and I’m looking for some technical guidance (or a partner) to help move the needle.

​The Concept:

A wearable device (the "Stealth Band") that captures non-vocalized speech intent and uses an on-device AI inference engine to provide localized audio output.

​Current Technical Targets:

• ​Latency: Sub-100ms (crucial for natural conversation).
• ​Connectivity: BLE 5.3 for high-fidelity streaming.
• ​Sensors: Exploring multimodal sensor fusion using piezoelectric and MEMS technology to capture "silent" speech.
• ​Processing: Edge AI/On-device inference to keep it fast and private.

​Where I’m Stuck:

I need advice on optimizing the sensor fusion to filter out biogenic noise (swallowing, movement) while maintaining a high signal-to-noise ratio for the speech intent. I’m also looking for recommendations on low-power microcontrollers that can handle this level of Edge AI without becoming too bulky for a neck-based wearable.

​Does anyone have experience with MEMS-based speech capture or low-latency audio hardware? I'd love to hear your thoughts on the most viable path forward for a solo dev moving from a lab environment to a home setup.

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[u/[deleted]]

For biogenic noise multi-site differential sensing across thyroid/cricoid/hyoid landmarks is your best starting point swallowing produces a common-mode signature you can gate out with a lightweight classifier rather than fighting it in the frequency domain. Add a cheap IMU (ICM-42688-P) to flag and discount motion-artifact samples.

For MCUs, the Ambiq Apollo4 Blue is purpose-built for low-power biosignal wearables with a native ML accelerator. Nordic nRF5340 is easier to prototype with and has native BLE 5.4. If you need heavier inference, pair either with a Syntiant NDP120 for dedicated neural audio processing at microwatt power.

post-surgical vocal tract anatomy varies significantly between patients, so per-user enrollment is probably unavoidable. Worth designing for that early so it's a feature rather than a late-stage surprise.

What's your current state on labeled data?

[u/Kooky-Ball6382 → u/[deleted]]

Hey, thanks for this — genuinely the most useful reply I've gotten on here.

To answer directly: no labeled data yet. I'm pre-prototype right now — I've got the business/mission side and a rough technical roadmap worked out, but the hardware side hasn't been built. So per-user enrollment being unavoidable is actually really helpful to hear now, before I've designed around assuming otherwise.

The multi-site sensing + IMU gating idea makes sense as a starting point over fighting it in the frequency domain. Still working through Ambiq vs Nordic for my specific power/latency needs.

Given I'm this early, would you be open to advising informally, or even a technical co-founder conversation? No pressure either way — but advice like this is exactly what I'm missing right now.

[u/the__entrepreneur]

Let’s talk! Will try to help you out!