WHISTRESS: Enriching Transcriptions with Sentence Stress Detection
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.
68354dbfb524d75f61faf04e
Iddo Yosha
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2025-05-27T15:49:57.797Z
658436f5c73f74776b19198a
/avatars/3f1d76af6fc0405d663c9294318fe83e.svg
Iddo Yosha
user
iyosha
68354dbfb524d75f61faf04f
Dorin Shteyman
claimed_verified
2025-05-27T15:49:53.004Z
65a8eb64c5ffe1d01910ab3e
/avatars/3a11fe375c4c469c6ef5272eaeb4ff67.svg
Dorin Shteyman
user
DorinSht
68354dbfb524d75f61faf050
Yossi Adi
WHISTRESS: Enriching Transcriptions with Sentence Stress Detection
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.
WHISTRESS is an alignment-free method for sentence stress detection trained on synthetic data, outperforming existing methods and generalizing well to diverse benchmarks.
alignment-free sentence stress detection TINYSTRESS-15K synthetic training data zero-shot generalization