Neurolexi DE
Research

Objective naming latency — scientifically validated.

Neurolexi measures speech onset after a picture automatically and precisely. The method is initial-phoneme-based, runs without heavy frequency analysis, and is backed by several peer-reviewed studies with the FHNW.

165
study participants
9,029
recordings analysed
16
initial phonemes studied
4
peer-reviewed publications

Data basis: 9,029 recordings in Swiss and Standard German · 134 healthy and 31 people with aphasia · 16 initial phonemes with ≥ 100 recordings each (Rickert et al. 2026).

How it works

Closer to speech onset — even for weak initial sounds

  • Close to the gold standardThe automatic measurement stays close to the manual expert measurement.
  • Peer-reviewedDeveloped with the FHNW and backed by four scientific publications.
  • Phoneme-specificReliably solves the problem of low-energy, voiceless onsets.
  • No frequency analysisSix optimised features with a reference time and detection window — no heavy spectral analysis needed.
Neurolexi measures naming latency
Clinical research

Built on years of peer-reviewed research

2026ARTICLE
Can an initial phoneme-based algorithm improve automatic naming latency detection during picture naming tasks? A feasibility study
Rickert E., Altermatt S. et al. — Biomedical Signal Processing and Control, Vol. 113, Part B, 2026, 108851.
FHNW_NL algorithm, validated on 9,029 recordings (Swiss & Standard German; 134 healthy and 31 people with aphasia): phoneme-specific detection of speech onset, validated against the manual gold-standard measurement.
Publication →
2021EMBC
Evaluation of the potential of automatic naming latency detection for different initial phonemes during picture naming task
Park S., Altermatt S. et al. — 43rd Annual Int. Conf. of the IEEE EMBS (EMBC), 2021, pp. 945–950.
Shows a strong positive correlation between automatic and manual latency, and that accuracy depends on the initial phoneme (voiced/voiceless) — the basis for phoneme-specific optimisation.
Publication →
2020EMBEC
Automatic detection of naming latency from aphasia patients – using an extended threshold-based method
Altermatt S., Kuntner K., Rickert E. et al. — Conf. Proc. EMBEC, Nov 2020, p. 71.
First threshold-based method for automatic naming-latency detection; tested on 272 AAT recordings from 8 people with aphasia against the manual gold standard.
Conference paper
2020EMBEC
Patient-friendly speech recognition feedback for aphasia patients
Wyss S., Rickert E., Altermatt S. et al. — Conf. Proc. EMBEC, Nov 2020, p. 283.
Patient-friendly word feedback via speech recognition — cuts the word-error rate from 4.39% to about 2.0% and underpins the live check of the spoken word in the app.
Conference paper

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