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Tamar I. Regev

dblp:239/2983 · also Tamar Regev · DBLP profile ↗
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4ranked-venue papers
1as first author
4since 2021 · last 2026
0000-0003-0639-0890ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 4 · 1 first-author · 4 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
4 papers
Speech recognition and synthesis · 65% Language models and text generation · 18% Information extraction and text analysis · 17%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Natural language and speech › Speech recognition and synthesis › speech analysis
prosody
1.922026
What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channels · ACL (1) 2026
Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent · ACL (1) 2025
Natural language and speech › Speech recognition and synthesis › text-to-speech synthesis
prosody modeling
1.522025
The time scale of redundancy between prosody and linguistic context · ACL (1) 2025
Quantifying the redundancy between prosody and text · EMNLP 2023
Natural language and speech › Language models and text generation › natural language understanding
linguistic knowledge in language models
0.712023
Quantifying the redundancy between prosody and text · EMNLP 2023
Natural language and speech › Language models and text generation › natural language understanding
text semantics
0.312026
What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channels · ACL (1) 2026

Methods — techniques the papers use, named apart from their topics

information-theoretic analysis · 1.9information theory · 0.9word embeddings · 0.7large language model · 0.7
YearPublicationVenuePosition
2026 What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channels
abstract
Aditya Yadavalli, Tiago Pimentel, Tamar I Regev, Ethan Gotlieb Wilcox, Alex Warstadt. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Aditya Yadavalli, Tiago Pimentel, Tamar I. Regev, Ethan Wilcox, Alex Warstadt
ACL (1)3
2025 The time scale of redundancy between prosody and linguistic context
abstract
Tamar I Regev, Chiebuka Ohams, Shaylee Xie, Lukas Wolf, Evelina Fedorenko, Alex Warstadt, Ethan Wilcox, Tiago Pimentel. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.
Tamar I. Regev, Chiebuka Ohams, Shaylee Xie, Lukas Wolf, Evelina Fedorenko, Alex Warstadt, Ethan Wilcox, Tiago Pimentel
ACL (1)1
2025 Using Information Theory to Characterize Prosodic Typology: The Case of Tone, Pitch-Accent and Stress-Accent
abstract
Ethan Wilcox, Cui Ding, Giovanni Acampa, Tiago Pimentel, Alex Warstadt, Tamar I Regev. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2025.
Ethan Wilcox, Cui Ding, Giovanni Acampa, Tiago Pimentel, Alex Warstadt, Tamar I. Regev
ACL (1)6
2023 Quantifying the redundancy between prosody and text
abstract
Prosody-the suprasegmental component of speech, including pitch, loudness, and tempocarries critical aspects of meaning.However, the relationship between the information conveyed by prosody vs. by the words themselves remains poorly understood.We use large language models (LLMs) to estimate how much information is redundant between prosody and the words themselves.Using a large spoken corpus of English audiobooks, we extract prosodic features aligned to individual words and test how well they can be predicted from LLM embeddings, compared to non-contextual word embeddings.We find a high degree of redundancy between the information carried by the words and prosodic information across several prosodic features, including intensity, duration, pauses, and pitch contours.Furthermore, a word's prosodic information is redundant with both the word itself and the context preceding as well as following it.Still, we observe that prosodic features can not be fully predicted from text, suggesting that prosody carries information above and beyond the words.Along with this paper, we release a general-purpose data processing pipeline for quantifying the relationship between linguistic information and extra-linguistic features.https://github.com/lu-wo/ quantifying-redundancy
Lukas Wolf, Tiago Pimentel, Evelina Fedorenko, Ryan Cotterell, Alex Warstadt, Ethan Wilcox, Tamar I. Regev
EMNLP7