CoSpeak hears the signal beneath the words: pitch, cadence, the texture of a voice. It infers emotional state in real time and answers in a tone that fits the moment. English and Spanish, built for clinical settings.
Most voice systems treat speech as a transcript to be parsed. CoSpeak treats it as a signal carrying emotional information the words alone discard. Four stages run through every turn.
Paralinguistic cues pulled from the raw waveform, independent of the words. Pitch contour, speaking rate, voice quality, spectral shape.
Self-supervised speech models place the utterance on a continuous valence and arousal plane, not one of a few rigid labels.
The reply is shaped by the inferred state. The system decides when to mirror, when to complement, and when to validate before offering anything else.
The response is voiced with prosody that fits the register. Pace, warmth, and pitch are set deliberately, never flattened into one neutral tone.
Figures are from controlled pilot deployments and an evaluation set, not population-level claims. Accuracy varies by language, recording conditions, and clinical context. Reported here for transparency, with full methodology available on request.
Each product applies the same sense-respond loop to a different communication problem. The shared layer reads the voice; the difference is what each one does with what it hears.
The core agent. Real-time recognition, healthcare-domain understanding, and emotionally adaptive spoken replies in English and Spanish. It answers from uploaded guidelines and patient context, in the register the moment calls for.
Voice and video analytics applied to first contact. Empathia reads emotional state during intake, adapts its questioning, and cut intake time by roughly a third in pilot while raising reported patient satisfaction.
A template-driven scribe that turns a recorded encounter into a structured, compliance-ready document. Practice-specific formats, terminology recognition, an audit trail on every edit.
platform.cospeak.ai →A conversational companion a person can talk to during stress or in ordinary conversation. It understands emotional state through voice and responds with appropriate support. It does not diagnose, label, or screen for any condition by design.
The loop is built on a defined stack at each stage, drawn from peer-reviewed speech and affective-computing work and validated in our own deployments.
Affective information lives in pitch, cadence, voice quality, and spectral shape long before it reaches the words. We extract a defined acoustic feature set and pass it to self-supervised speech representations rather than relying on transcripts alone.
Response generation is grounded in empathetic-dialogue and emotional-support corpora, then voiced through controllable emotional text-to-speech so prosody carries the same intent as the wording. Mirror or complement is an explicit decision, not an accident of sampling.
Emotion is modeled on a continuous valence and arousal plane rather than sorted into a handful of fixed labels. This holds up better across languages, where the same felt state surfaces through different acoustic patterns.
Real-time on-device affect sensing, multilingual emotion recognition, longitudinal tracking, and tight coupling with large language models. We track this landscape closely, including the line the EU AI Act draws around emotion recognition.
English and Spanish, with emotion inference tuned to the prosody of each. The model is not ported from one to the other; the acoustic conventions differ, so the reading does too.
Tuned on clinical and intake speech across accents. Stress-timed rhythm means arousal shows up first in tempo and loudness, so the reading weights rate and energy dynamics heavily.
Trained for the wider pitch range and syllable-timed rhythm of spoken Spanish. The same arousal reads differently, so pitch contour and vowel duration are weighted to avoid mistaking expressiveness for distress.
Cross-lingual emotion recognition is an open research problem precisely because expressiveness is cultural. Treating each language on its own acoustic terms is the difference between a system that reads feeling and one that mislabels a lively speaker as agitated.
CoSpeak interprets emotional state the way a person registers tone in a conversation, and uses that to respond appropriately. It does not identify, label, or screen for any clinical condition. That boundary is a deliberate design and product decision, not a missing feature.
The distinction matters. Diagnostic voice-biomarker products have faced hard reckonings, including notable shutdowns in 2026, while emotion-recognition systems sit under tightening regulation such as the EU AI Act. Building for emotional understanding and supportive response, rather than diagnosis, keeps the system useful, defensible, and honest about what voice can and cannot reliably tell us.
Pilots run in clinical, intake, and support settings. Bring a workflow; we will show you where the loop fits.