Harmonica evidence room
The proprietary senior voice dataset, production audio models, and real in-home outcomes behind Olympia.
Every day in the home makes the dataset harder to copy.
The asset is not generic speech. It is longitudinal senior conversation, wake words, health concerns, needs, memories, product love, and model failures from real rooms.
vulnerability
shared
stories
morphisation
Data through Jun 24, 2026, from Olympia conversations in real homes.
Top in-home users have generated 130 hours of measured conversation.
Conversation totals through Jun 24, 2026, from Olympia devices in real homes.
Users say it plainly, then the conversations prove it.
Direct testimonials sit alongside the conversation moments behind the aggregate results.
Bill describes the companionship and everyday usefulness of Olympia in his own words.
George shows the product pull that is hard to fake: a real person, in a real home, talking about what changed.
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Proprietary models trained on senior voices, real rooms, and daily routines.
Olympia converts ordinary speech into longitudinal baselines across acoustic, linguistic, prosodic, emotional, and respiratory signals. The models are already learning from real conversations and improve as the dataset grows.
Pitch, shimmer, jitter, harmonic noise, articulation, and vocal stability.
Vocabulary richness, content density, repetition, coherence, and sentence complexity.
Speaking rate, pause structure, turn latency, breathiness, cough flags, and vocal energy.
Micro-instability against a personal baseline can reveal fatigue, vocal strain, neurological change, or respiratory stress.
Two real voice samples produce visibly different acoustic profiles.
User 1 sounds healthy: sustained speech, expressive pitch, and clear spectral energy. User 2 shows early warning signs: more interrupted speech, narrower prosody, and a darker acoustic profile.
Anonymized comparison from the voice samples in this section. Pause rate is lower-is-better; other rows are higher-is-stronger.
Vocal biomarkers are one of the few scalable ways to measure cognition, respiratory clarity, emotional state, and motor control outside the clinic. Dementia changes word retrieval, repetition, coherence, and pause structure; respiratory and frailty changes show up in breathiness, cough events, harmonic noise, and vocal energy.
Olympia’s advantage is longitudinal context: the same person, same room, same relationship, measured repeatedly against their own baseline.
The wake word has to survive the real home.
Older adults do not speak like benchmark datasets, and their homes are not quiet labs. Olympia has to hear a soft call from across the room, ignore television and kitchen noise, and recover gracefully when the first attempt is imperfect.
Our wake-word model pairs high-recall algorithms with a production learning loop: real user voices, real room acoustics, repeated registrations, failed attempts, and edge cases flowing back into evaluation and training.
Recall on real wake-word registration clips; anonymized vendor labels.
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Speaker ID keeps the biomarker dataset clean by distinguishing the primary user from a spouse, adult child, carer, visitor, or television.
Measured on 95k real-world speaker-identification examples.
Olympia uses best-in-market ASR, TTS, and LLMs today, while the strategic upside is training and adapting models on senior speech from real homes.
Studies show ASR underperforms on senior data because older-adult speech has different acoustic and linguistic characteristics and less representative training data.