Using Prosody to Predict Syntactic Structure

• URL: http://arxiv.org/abs/2608.30260v1
• Date: Mon, 31 Aug 2026 05:09:36 GMT
• Title: Using Prosody to Predict Syntactic Structure
• Abstract summary: We investigate the syntax-prosody interface through an information-theoretic lens.<n>We provide a general-purpose framework for estimating the quantity of prosodic features over large speech-text corpora.<n>Our results demonstrate that prosody contains measurable syntactic information, with prosodic features reducing syntactic uncertainty in spontaneous conversations by up to 10.2%.
• Score: 21.97838341845281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While it is well-established that prosody carries crucial cues for syntactic structure, the degree and nature of correspondence between these two domains remains contested. We investigate the syntax-prosody interface through an information-theoretic lens, quantifying the interaction between prosodic features and syntactic representations as their mutual information. We provide a general-purpose framework for estimating this quantity over large speech-text corpora using multimodal language models. Our framework is structure-agnostic and modular, insofar as it can be used to measure the contributions of individual prosodic features or components of structure. We evaluate the syntax-prosody relationship for two features (word duration and inter-word pauses) across two domains--read audiobooks and spontaneous conversations--both in English. Our results demonstrate that prosody contains measurable syntactic information, with prosodic features reducing syntactic uncertainty in spontaneous conversations by up to 10.2%. Our findings offer new empirical support for several theoretical accounts of the syntax-prosody interface.

Related papers

• Information Dynamics of Language Communication [3.2562256911091407]
  We introduce an information-theoretic framework that quantifies the directed flow of semantic content between interlocutors.<n>Across four experiments we show that the framework detects reduced information flow in cognitively rigid dialogue.<n>This framework opens new avenues for studying information dynamics in digital discourse, pedagogical interactions, clinical dialogues, and any domain in which the structure of linguistic exchange is of research relevance.
  arXiv  Detail & Related papers  (2026-06-29T10:31:58Z)
• Dialogues Aspect-based Sentiment Quadruple Extraction via Structural Entropy Minimization Partitioning [54.25737182568224]
  DiaASQ aims to extract all target-aspect-opinion-sentiment quadruples from a given multi-round, multi-participant dialogue.<n>We introduce a two-step framework for quadruple extraction: first extracting individual sentiment elements at the utterance level, then matching quadruples at the sub-dialogue level.
  arXiv  Detail & Related papers  (2025-08-07T04:22:17Z)
• Counting trees: A treebank-driven exploration of syntactic variation in speech and writing across languages [0.0]
  We define syntactic structures as delexicalized dependency (sub)trees and extract them from spoken and written Universal Dependencies treebanks.<n>For each corpus, we analyze the size, diversity, and distribution of syntactic inventories, their overlap across modalities, and the structures most characteristic of speech.<n>Results show that, across both languages, spoken corpora contain fewer and less diverse syntactic structures than their written counterparts.
  arXiv  Detail & Related papers  (2025-05-28T18:43:26Z)
• A Multi-view Discourse Framework for Integrating Semantic and Syntactic Features in Dialog Agents [0.0]
  Multiturn dialogue models aim to generate human-like responses by leveraging conversational context.<n>Existing methods often neglect the interactions between these utterances or treat all of them as equally significant.<n>This paper introduces a discourse-aware framework for response selection in retrieval-based dialogue systems.
  arXiv  Detail & Related papers  (2025-04-12T04:22:18Z)
• Quantifying the redundancy between prosody and text [67.07817268372743]
  We use large language models to estimate how much information is redundant between prosody and the words themselves. We find a high degree of redundancy between the information carried by the words and prosodic information across several prosodic features. Still, we observe that prosodic features can not be fully predicted from text, suggesting that prosody carries information above and beyond the words.
  arXiv  Detail & Related papers  (2023-11-28T21:15:24Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• Learning Disentangled Speech Representations [0.412484724941528]
  SynSpeech is a novel large-scale synthetic speech dataset designed to enable research on disentangled speech representations.<n>We present a framework to evaluate disentangled representation learning techniques, applying both linear probing and established supervised disentanglement metrics.<n>We find that SynSpeech facilitates benchmarking across a range of factors, achieving promising disentanglement of simpler features like gender and speaking style, while highlighting challenges in isolating complex attributes like speaker identity.
  arXiv  Detail & Related papers  (2023-11-04T04:54:17Z)
• Revisiting Conversation Discourse for Dialogue Disentanglement [88.3386821205896]
  We propose enhancing dialogue disentanglement by taking full advantage of the dialogue discourse characteristics. We develop a structure-aware framework to integrate the rich structural features for better modeling the conversational semantic context. Our work has great potential to facilitate broader multi-party multi-thread dialogue applications.
  arXiv  Detail & Related papers  (2023-06-06T19:17:47Z)
• PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition [63.51569687229681]
  We argue for the need to recognize the textual entailment relation of each proposition in a sentence individually. We propose PropSegmEnt, a corpus of over 45K propositions annotated by expert human raters. Our dataset structure resembles the tasks of (1) segmenting sentences within a document to the set of propositions, and (2) classifying the entailment relation of each proposition with respect to a different yet topically-aligned document.
  arXiv  Detail & Related papers  (2022-12-21T04:03:33Z)
• Syntactic representation learning for neural network based TTS with syntactic parse tree traversal [49.05471750563229]
  We propose a syntactic representation learning method based on syntactic parse tree to automatically utilize the syntactic structure information. Experimental results demonstrate the effectiveness of our proposed approach. For sentences with multiple syntactic parse trees, prosodic differences can be clearly perceived from the synthesized speeches.
  arXiv  Detail & Related papers  (2020-12-13T05:52:07Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.