Voice Screening Model
Non-invasive voice analysis demo (research use)

Anshika Agrahari

Predicted: Benign Actual: Not set

Patient info

Age
25
Gender
female
Recorded
2026-02-24 06:04:33.504986 (UTC)
Duration
6.23 sec

Model prediction

Predicted: Benign

Probabilities

  • Benign0.538
  • Malignant0.433
  • Normal0.028

Set actual diagnosis

Aggregate features

FeatureValue
f0_mean155.048708
f0_median155.339118
f0_min142.681156
f0_max172.989036
f0_sd6.407357
f0_range30.307880
jitter_local0.038615
jitter_rap0.016744
jitter_ppq50.013699
shimmer_local0.059104
shimmer_apq30.017858
shimmer_apq50.031130
shimmer_apq110.059324
hnr_mean15.734343
cpp_mean13.407224
spectral_centroid868.480421
spectral_flatness0.242390
zcr0.063113
spectral_rolloff_851566.970653
ltas_0_1k0.969265
ltas_1_2k0.020665
ltas_2_4k0.004175
ltas_4_8k0.004649

Key markers include F0 Max, Shimmer APQ3/APQ5, CPP, and LTAS 0–1k.

Segment predictions

#TimeLabelProbabilities
1 0.0s – 3.0s Malignant Benign: 0.100 Malignant: 0.894 Normal: 0.006
2 1.5s – 4.5s Benign Benign: 0.762 Malignant: 0.199 Normal: 0.039
3 3.0s – 6.0s Benign Benign: 0.753 Malignant: 0.207 Normal: 0.040
Disclaimer: This tool is for research purposes only. It must not be used for clinical diagnosis without validation.