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arXiv · 2606.23228

Acoustic Landmark Detector based on Conformer and HuBERT

Mateo Cámara·José Luis Blanco·Juan Ignacio Godino-Llorente·Jeung-Yoon Choi·Stefanie Shattuck-Hufnagel
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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.

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Mateo Cámara, José Luis Blanco, Juan Ignacio Godino-Llorente, Jeung-Yoon Choi, Stefanie Shattuck-Hufnagel. 2026-06-22. Acoustic Landmark Detector based on Conformer and HuBERT. https://arxiv.org/abs/2606.23228

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