title: WHISTRESS: Enriching Transcriptions with Sentence Stress Detection
summary: Spoken language conveys meaning not only through words but also through
  intonation, emotion, and emphasis. Sentence stress, the emphasis placed on
  specific words within a sentence, is crucial for conveying speaker intent and
  has been extensively studied in linguistics. In this work, we introduce
  WHISTRESS, an alignment-free approach for enhancing transcription systems with
  sentence stress detection. To support this task, we propose TINYSTRESS-15K, a
  scalable, synthetic training data for the task of sentence stress detection
  which resulted from a fully automated dataset creation process. We train
  WHISTRESS on TINYSTRESS-15K and evaluate it against several competitive
  baselines. Our results show that WHISTRESS outperforms existing methods while
  requiring no additional input priors during training or inference. Notably,
  despite being trained on synthetic data, WHISTRESS demonstrates strong
  zero-shot generalization across diverse benchmarks. Project page:
  https://pages.cs.huji.ac.il/adiyoss-lab/whistress.
paper_authors._id: 68354dbfb524d75f61faf04e
paper_authors.name: Iddo Yosha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T15:49:57.797Z
paper_authors._id: 658436f5c73f74776b19198a
paper_authors.avatarUrl: /avatars/3f1d76af6fc0405d663c9294318fe83e.svg
paper_authors.fullname: Iddo Yosha
paper_authors.type: user
paper_authors.user: iyosha
paper_authors._id: 68354dbfb524d75f61faf04f
paper_authors.name: Dorin Shteyman
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-05-27T15:49:53.004Z
paper_authors._id: 65a8eb64c5ffe1d01910ab3e
paper_authors.avatarUrl: /avatars/3a11fe375c4c469c6ef5272eaeb4ff67.svg
paper_authors.fullname: Dorin Shteyman
paper_authors.type: user
paper_authors.user: DorinSht
paper_authors._id: 68354dbfb524d75f61faf050
paper_authors.name: Yossi Adi
paper_title: WHISTRESS: Enriching Transcriptions with Sentence Stress Detection
paper_summary: Spoken language conveys meaning not only through words but also through
  intonation, emotion, and emphasis. Sentence stress, the emphasis placed on
  specific words within a sentence, is crucial for conveying speaker intent and
  has been extensively studied in linguistics. In this work, we introduce
  WHISTRESS, an alignment-free approach for enhancing transcription systems with
  sentence stress detection. To support this task, we propose TINYSTRESS-15K, a
  scalable, synthetic training data for the task of sentence stress detection
  which resulted from a fully automated dataset creation process. We train
  WHISTRESS on TINYSTRESS-15K and evaluate it against several competitive
  baselines. Our results show that WHISTRESS outperforms existing methods while
  requiring no additional input priors during training or inference. Notably,
  despite being trained on synthetic data, WHISTRESS demonstrates strong
  zero-shot generalization across diverse benchmarks. Project page:
  https://pages.cs.huji.ac.il/adiyoss-lab/whistress.
paper_ai_summary: WHISTRESS is an alignment-free method for sentence stress detection trained on synthetic data, outperforming existing methods and generalizing well to diverse benchmarks.
paper_ai_keywords: alignment-free sentence stress detection TINYSTRESS-15K synthetic training data zero-shot generalization