AI & ML
Off-target posterior mass: A training-free diagnostic for fine-tuning spoken language diarization probes
arXiv cs.SD0 views
arXiv:2611.13430v1 Announce Type: cross Abstract: 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.
Source：https://arxiv.org/abs/2611.13430PreviousAssessing Alignment in Automatic Piano Transcription Datasets: A Benchmark for Scoring MethodsNextEmilia2: A Large-Scale Dataset for Controllable Long-Form Speech Generation
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