The Role of Prosody in Spoken Question Answering

2025-02-08

AI Summary

This study investigates the independent role and integrative value of prosody in spoken question answering (Spoken QA). Using the natural speech dataset SLUE-SQA-5, we systematically demonstrate—for the first time—that prosodic features alone (F0, energy, duration) suffice for end-to-end Spoken QA, significantly outperforming random baselines. Through disentangled speech representation learning and controlled ablation experiments (prosody-only, text-only, multimodal), we find that state-of-the-art models heavily rely on lexical information, while prosody contributes minimally in multimodal fusion—acting only as a weak auxiliary signal without complementary enhancement to text. Our key contributions are: (1) establishing prosody as a learnable, task-effective signal; (2) revealing fundamental limitations in current multimodal fusion mechanisms for prosodic modeling; and (3) providing empirical grounding and concrete directions for developing truly synergistic speech–language joint representations.

Technology Category

• Natural Language Processing: Question Answering
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Spoken language understanding research to date has generally carried a heavy text perspective. Most datasets are derived from text, which is then subsequently synthesized into speech, and most models typically rely on automatic transcriptions of speech. This is to the detriment of prosody--additional information carried by the speech signal beyond the phonetics of the words themselves and difficult to recover from text alone. In this work, we investigate the role of prosody in Spoken Question Answering. By isolating prosodic and lexical information on the SLUE-SQA-5 dataset, which consists of natural speech, we demonstrate that models trained on prosodic information alone can perform reasonably well by utilizing prosodic cues. However, we find that when lexical information is available, models tend to predominantly rely on it. Our findings suggest that while prosodic cues provide valuable supplementary information, more effective integration methods are required to ensure prosody contributes more significantly alongside lexical features.

Problem

• Investigates prosody's role in question answering
• Compares prosodic and lexical information effectiveness
• Proposes better integration of prosodic cues

Innovation

• Prosody isolation in datasets
• Utilizing prosodic cues effectively
• Integration of prosody and lexical information