Play the audio and follow the subtitles. Each word is colored by the speaker that method assigned; a red underline means it disagrees with ground truth.
Source: Harper Valley Bank (CC BY 4.0) — no patient audio.
What each method knows:AWS and W+ECAPA self-enrolled are fully blind — profiles
come from the recording's own clusters, with no outside reference audio. W+ECAPA enrolled and FT v2
were given about 20 s of reference audio from each person's clean channel — available for care partners
in the archive, rarely for patients. Compare the first two for a fair test, the last two to see what enrollment adds.
Scores
Subtitles
Extraction quality — pretrained vs our fine-tunes
A different question, and the one extraction is actually built for: on a natural call, how cleanly does the model pull out one person?
Three held-out Harper Valley calls, agent enrolled. Scores are SI-SNR in dB while the target speaks alone (higher is better),
and the other speaker's residue (lower is better). The pretrained model was the best of three off-the-shelf options; the fine-tunes are ours.
FT v2 — extracted audio
The other methods only label; they never split the audio. FT v2 actually extracts it — this is the per-person output.
Speed
Measured end to end on the 107 s two-men call, one laptop CPU, no GPU. Model loading (about 8 s total) excluded.
stage
time
vs real time
Whisper large-v3-turbo (words)
27.5 s
3.9x
pyannote (clustering)
96.3 s
1.1x
ECAPA (423 sliding windows)
9.3 s
11.5x
SpeakerBeam FT v2 (one speaker)
25.5 s
4.2x
Pipeline totals for 107 s of audio:
W+ECAPA enrolled 36.8 s (2.9x) · W+ECAPA self-enrolled 133.1 s (0.8x) · plus FT v2 on both speakers 184.1 s (0.6x).
pyannote is the bottleneck — it costs more than everything else combined. A CP voice profile removes it from the pipeline,
which is why enrollment is worth about 3.6x in speed on top of what it does for accuracy.