Standard Voice Recognition
Our Voice Recognition
Transcribe common phrases
Work with major languages
Run on cloud infrastructure
Predict likely word sequences
Recognize out-of-vocabulary words
Handle dialects and low- resource languages
Operate on edge devices in real time
Understand the actual sounds being spoken
FlexSR verification and spotting layer for speech AI confirms whether spoken audio matches an expected word, spots keywords in real time, and flags errors in existing ASR output. All on-device, with no cloud dependency and no per-language training, at a fraction of the compute cost of statistical or LLM-based ASR.
Why is our product different?
FlexSR is a verification and spotting layer for speech AI, not a full transcription replacement for Whisper, Google or AWS.
Our product is built on a patented phonological feature model.
Human language can be broken down and understood by 19 phonological features.
Speech is produced by a defined set of articulatory actions. FlexSR's model resolves any speech sound into these features — voicing, lip closure, tongue height, nasality — and every word in every language is a sequence drawn from that set.
The colored outlines simply highlight the key parts: lips, teeth, tongue, the roof of the mouth, and the throat.
Tongue moves into a completely different shape and position every time the person makes a different sound.
This scan captures that movement happening live — proof of exactly how the human body physically creates speech.
This is a live scan of a person's head — captured while they were talking.
How does it actually work?
Spectral Analysis of Features - Example "Fußball ist Spitze"
1. your voice
2. How the device listens
3.Speech clues
4. Small speech
sounds
The raw recording. Air pressure over time, exactly as the microphone captured it. Nothing has been interpreted yet.
Spectral analysis. The single waveform is separated into its component frequencies so the energy in each band can be tracked moment by moment. The coloured lines are those tracks.
The acoustic cues pulled out of that spectrum: the specific patterns that reveal what the vocal tract was doing — voicing, a burst of friction, a nasal resonance, a change in tongue position.
The feature grid. Each cue is resolved into the 19 phonological features, marked present or absent at every point in time. This is a physical description of what was said, not yet a guess at the words.
5.guessing the sounds
6.Matching to words
7.Final Answer
The features are grouped into the individual sounds they most likely form, shown here in phonetic notation. This is the first stage where the system infers rather than measures.
Those predicted sounds are matched against a dictionary to find the words they correspond to.
The output text. This is all a conventional system hands back, and by this point every judgement made at stages 5 and 6 is baked in and invisible.
Built For All Environments
Data & Device Security
No cloud dependency: all processing happens on-device
No audio or data leaves the device or local network
Runs on standard hardware, including Raspberry Pi
Standards & Assurance
DASA validated: Fundable / Unfunded, 2025
Ongoing trial with Police Scotland's interviews
Worldwide Patents granted
Speech Recognition Capabilities
1
Keyword & Phrase Spotting
You need real-time detection of trigger words/phrases to populate forms or fields automatically
The solution: Instantly detects and flags specific keywords, feeding structured data directly to your system (like ATAK integration)
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2
Verification
You need to confirm someone said a specific word or phrase (e.g., a callsign, confirm button press) without capturing everything they said
The solution: Checks if spoken audio matches your expected phrase - no transcription needed
3
Error Highlighting
Standard speech-to-text transcription is never 100% accurate - you don't know where it went wrong
The solution: Flags high-risk sections where the transcription likely diverged from what was actually said