← 研踪 PaperTrailarXiv:2611.13430 · v1
Off-target posterior mass: A training-free diagnostic for fine-tuning spoken language diarization probes
首次提交 2026-11-10 09:15
版本更新 2026-11-10 09:15
雷达观测 2026-11-11 11:04
摘要
Spoken Language Diarization (SLD) models provide frame-level language information of a speech recording. SLD modeling pipeline includes feature extraction from pre-trained speech models followed by clustering. Fine-tuning pre-trained speech models has shown performance gains for SLD with its magnitude being dataset dependent. Off-target Posterior Mass (OTPM), computed based on posterior language identification probabilities is proposed as a diagnostic metric to forecast performance gains from SLD fine-tuning. OTPM obtains positive correlation with probe gains, 0.56 on unseen synthetic bilingual data and 0.23 on real-world corpora. These results indicate promising potential of OTPM as a diagnostic metric for SLD.
作者备注：10 pages, 2 figures, 3 tables. Submitted to JASA Express Letters
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v1 · 2026-11-10 09:15 · 最新观测版本
Off-target posterior mass: A training-free diagnostic for fine-tuning spoken language diarization probes
Spoken Language Diarization (SLD) models provide frame-level language information of a speech recording. SLD modeling pipeline includes feature extraction from pre-trained speech models followed by clustering. Fine-tuning pre-trained speech models has shown performance gains for SLD with its magnitude being dataset dependent. Off-target Posterior Mass (OTPM), computed based on posterior language identification probabilities is proposed as a diagnostic metric to forecast performance gains from SLD fine-tuning. OTPM obtains positive correlation with probe gains, 0.56 on unseen synthetic bilingual data and 0.23 on real-world corpora. These results indicate promising potential of OTPM as a diagnostic metric for SLD.
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