OpenMic runs a multi-layer acoustic engine on every syllable you speak — real-time signal analysis, speech recognition, and weighted scoring that measures pre-articulatory motor stability at the syllable level. In any browser.
A browser-based speech practice console. OpenMic listens through your microphone and runs a layered analysis pipeline — acoustic feature extraction, speech recognition, and weighted scoring — producing a per-syllable signal profile in real time, session over session.
Acoustic events — onset repetitions, prolonged voicing, intensity anomalies — surfaced from the audio signal in real time. Every syllable scored for acoustic stability across multiple analysis layers.
Practice any text. Words advance as you speak. The scoring engine runs in parallel on every word.
Session history, trend lines, and per-word scoring maps that update every session.
Your camera stays on while you speak so you can watch your own delivery in real time.
Language-agnostic acoustic engine with cloud-based speech recognition. Supports dozens of languages and regional variants.
OpenMic doesn't run a single algorithm on your speech. It runs a layered analysis pipeline — three independent systems that each see different things in the audio signal. Their outputs converge through weighted scoring into a single per-syllable signal score. That score is what you see. The layers underneath are what make session-over-session score comparison reliable.
U.S. Provisional 64/016,001 · Filed March 24, 2026The Disfluency Feature Stream runs at ~60 frames per second directly on the audio signal. Every frame is classified — silent, building, or voiced — producing a continuous stream of acoustic features: intensity, onset count, voiced duration, signal-stability flags. This is the raw measurement layer. No transcription, no interpretation — just signal.
A cloud-based speech recognition layer operates independently from DFS. It returns what was said — phoneme identity, phoneme-level accuracy scores, word boundaries, and timing. Where DFS tells you how the speech sounded acoustically, SR tells you what was produced linguistically. Two independent verdicts on the same utterance.
DFS features and SR output converge through a weighted scoring layer with defined, calibrated weights. The result is a single per-syllable signal score — the PAD score — that reflects acoustic stability, articulatory accuracy, and timing. This is what the speaker sees. The layers underneath are what make session-over-session comparison reliable.
DFS and SR see different things. DFS detects that something acoustically unstable happened — a repeated onset, an intensity spike, a voiced duration anomaly. SR detects that the phoneme produced didn't match the target, or that a word was repeated. When both layers flag the same syllable, the scoring confidence is high. When they disagree, the system surfaces the disagreement for review.
This is what makes session-over-session score comparison reliable. The score isn't one algorithm's opinion — it's the convergence of two.
Each session produces a six-axis signal profile — Acoustic Pressure, Vocal Steadiness, Onset Consistency, Speech Rate, Session Consistency, and Phoneme Range. Stacked across sessions, the shape shows session-by-session trajectory. Drag to rotate, scroll to zoom.
The space between two sounds is where voicing continuity is most fragile — and where it can be practiced. Sound Bridge measures voicing continuity across every phoneme transition you produce: frame-accurate, microphone-only, no wearables.
Twenty-eight sound pairs across four difficulty tiers. The PAD engine measures the bridge as three independent phases — hold, slide, landing — and surfaces exactly where the bridge held and where it didn't. Pitch trajectory and energy envelope drawn over every attempt.
Excessive articulatory co-contraction — antagonist muscles fighting each other when a sound should flow — is observable in the acoustic signal. Articulation Trainer makes that visible.
Pick a word or type your own. Speak it naturally. Each sound slot lights up in sequence — blue for passed, red for repeated attempts or high effort. The target is all green: minimal-effort production, phoneme by phoneme.
Eight voice-driven practice modes, all running on the same layered engine. Each targets a different aspect of speech-motor control. Microphone and browser only — no downloads.
Choose a phrase. Press the mic. Speak naturally. Syllable blobs light up in real time — blue for soft, green for medium, orange for loud. Your PAD score shows after each round. Difficulty adjusts automatically.
Easy onset and prolonged speech are well-studied speech-production patterns. Rainbow Syllables structures practice around sustained, controlled voicing across an entire phrase.
Phrase-level production exercises the full basal ganglia–thalamocortical loop. Each syllable requires the putamen to gate the next motor plan in sequence.
Two sounds appear on screen. Say both without breaking your voice between them. 28 sound pairs across four difficulty levels. Start easy, work up.
Continuous phonation and coarticulation are well-studied speech-motor patterns. Sound Bridge isolates the transition between two sounds and measures voicing continuity across it.
Coarticulation is controlled by the premotor cortex. Sound Bridge directly trains feedforward control described in the DIVA model of speech production.
Type your scariest word. Hit start. Say it. Say it again. Watch your score climb and the climber rise with each repetition. The word that felt impossible becomes the word you've said fifty times.
Structured repetition of feared words draws on established speech-practice principles (Sheehan, Van Riper). Summit applies structured exposure — confront the word through repetition.
Word-specific fear engages the amygdala, which modulates the basal ganglia gating system. Repeated voluntary production engages the amygdala–basal ganglia gating circuit through structured exposure.
Pick a word from the bank or type your own. Tap Speak and say it naturally. Each sound slot lights up in sequence — blue means it passed, red means it detected repeated attempts or high effort. Aim for all green. Less effort = better score.
Effort monitoring and proprioceptive awareness are well-studied speech-motor principles. Articulation Trainer surfaces per-phoneme effort visually — showing which articulators are over-engaged.
Excessive co-contraction of antagonist muscles at the articulatory level is observable acoustically. Per-phoneme effort visualization surfaces motor overflow patterns that are otherwise only observable by ear.
Pads appear with a target volume zone. Make a sound and land in the zone. Green = nailed it. Difficulty increases as you improve. Three modes: hold, alternate, and burst.
Motor learning principles — specificity of practice, distributed practice, variable practice. Rhythm Pad trains proprioceptive control through volume targeting.
Volume regulation targets the M1 orofacial region and cerebellar-cortical coordination for sound intensity mapping.
Watch the beat indicator. When it hits the zone, make your sound. Start slow, speed up. The game scores how close you land to each beat.
Rhythmic cueing externalizes the timing signal that the basal ganglia typically provides internally — providing an alternative timing input pathway.
External rhythm engages the supplementary motor area via the cerebellum, providing an alternative timing pathway alongside the basal ganglia–SMA loop.
Watch bubbles move in wave patterns. When one enters the green zone, make a sound at the right volume. Miss the zone and it floats away. Speed changes as you level up.
Dual-task practice — producing controlled speech while tracking a moving target — trains attention resource management under cognitive load.
Simultaneous visuomotor tracking and vocal output engages prefrontal executive control alongside the speech-motor circuit, training the system to perform under divided attention.
Practice happens between sessions, not just during them. OpenMic gives your clients an independent practice tool and gives you the session data from between appointments.
Share OpenMic with clients. Review per-syllable session data, trend lines, and challenge-word PAD maps between appointments.
Session-over-session PAD scores, acoustic event logs, and per-word history. Data export in CSV.
Caseload management, session-over-session score tracking, per-phoneme analysis, and a six-axis signal profile — all from the engine's scoring output.
View SLP dashboard ↗Integrating the OpenMic engine inside your platform or product? B2B licensing and white-label arrangements available. Contact for scope and pricing.
Research protocol FP-PILOT-001 · academic IRB pathway in development. Contact for research partnership inquiries.
Full four-level resolution breakdown — session, word, syllable, phoneme — with the complete OpenMic signal record. For clinical and technical partners.
View capabilities deck ↗Research by Per Alm and others locates the neurological origin of stuttering in the basal ganglia–SMA timing loop — not at articulation, but in pre-articulatory motor planning. The motor plan destabilizes before the mouth moves.
Most speech tools measure at or after articulation. OpenMic's engine analyzes the pre-articulatory window acoustically, treating voice onset time and formant stability as observable signal features upstream of articulation.
The engine targets instability at the Pre-SMA → SMA gate.
Phonetic complexity alone doesn't predict where stuttering occurs. Word-specific neural history shapes pre-articulatory motor planning. A familiar word carries a different motor planning load than a novel one — independent of phonetic difficulty.
OpenMic tracks per-word scoring across sessions. The scoring map shows per-word PAD trajectories session by session.
FluentPlay is in initial discussions with NIRx about developing a wearable fNIRS protocol for pre-SMA hemodynamic monitoring during speech planning tasks.
No active protocol is underway. If this work proceeds, PAD scores would be compared against fNIRS-measured pre-SMA activation. If validated, PAD could serve as a non-invasive acoustic correlate of pre-articulatory motor activity.
Every game shares the same audio engine. The microphone feeds a real-time analysis pipeline — the DFS runs at approximately 60 frames per second, classifying every frame into a disfluency feature stream. A parallel cloud-based speech recognition layer provides phoneme-level accuracy. Both streams converge into the scoring layer. No audio is recorded. Nothing is stored.
View pipeline walkthrough ↗FluentPlay is HIPAA-compliant by architecture. No personally identifiable or protected health information is created, collected, or stored at any layer. Audio is processed in real time through cloud-based speech recognition with no retention. No accounts or identifiers are required to use OpenMic.
Research protocol FP-PILOT-001 · academic IRB pathway in development. Research partnership inquiries welcome — contact below.
Will Carbone is the founder of FluentPlay Technologies. He's stuttered since childhood. Before FluentPlay, he spent more than a decade inside clinical and commercial biotech — building, monitoring, and stress-testing the systems that take drug molecules from synthesis to verified purity. Extraction, purification, analytical measurement, quality control from bench to commercial scale. The discipline of making invisible things legible through rigorous data.
When he looked at what existed for people who stutter, he found tools that hadn't kept pace with the neuroscience. The research is clear: stuttering involves timing instability in pre-articulatory motor planning. The tools treated it as something else.
He left biotech and built the practice tool the field was missing.
Research protocol FP-PILOT-001 · academic IRB pathway in development.
Contact for research partnership inquiries.
Engine licensing, white-label deployments, and enterprise agreements handled directly. No RFPs — just a conversation.