WHISTRESS: Enriching Transcriptions with Sentence Stress Detection
Authors: name: Iddo Yosha, name: Dorin Shteyman, name: Yossi Adi

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.