Abstract:Acoustic landmarks (abrupt acoustic changes tied to speech events) offer a linguistically grounded representation for speech analysis. We study automatic landmark detection with Conformer models, evaluating 14 configurations spanning architecture, loss, label representation, feature extractor, and data conditions on 1 839 manually annotated utterances with eight landmark types. We propose Gaussian soft labels with per-class temporal spread (sigma=10-20 ms), improving F1-at-20 ms by 7.0% absolute vs. hard labels by modeling annotation variability. Frozen HuBERT features perform best without fine-tuning (F1-at-20 ms=0.77). Stops and fricatives are reliable (F1>0.80), while vowels remain challenging (F1 approx 0.55). On our corpus, our system reaches a 13.8% Landmark Error Rate (LER). This is not directly comparable to AutoLandmark (31.3%) or SpeechMark (56.5%), evaluated on a different corpus and metric. Per-class trends show detectability increases with event abruptness, consistent with Stevens' theory.
From: Mateo Cámara [view email]
[v1]
Mon, 22 Jun 2026 12:16:11 UTC (163 KB)
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