[Paper] What Do Prosody and Text Convey? Characterizing How Meaningful Information is Distributed Across Multiple Channels

Source: arXiv - 2512.16832v1

Overview

This paper tackles a surprisingly practical question: how much meaning do we get from how something is said versus what is said? By treating prosody (the rhythm, pitch, and intonation of speech) as a separate communication channel, the authors use large speech‑and‑language models to measure exactly how much information about sarcasm, emotion, and questionhood lives in the audio signal that isn’t already present in the transcript. Their findings show that, for many affective cues, prosody carries an order of magnitude more information than text alone—especially when we can’t rely on broader conversational context.

Key Contributions

• Information‑theoretic framework for quantifying the mutual information between a semantic dimension (e.g., sarcasm) and each communication channel (audio vs. text).
• Adaptation of large pretrained speech and language models (e.g., Whisper, BERT) to estimate these mutual information values without hand‑crafted features.
• Empirical analysis on real‑world corpora (TV shows and podcasts) covering three semantic dimensions: sarcasm, emotion, and questionhood.
• Demonstration that prosody dominates text for sarcasm and emotion detection when only the current utterance is available.
• Roadmap for extending the approach to other meaning dimensions, multimodal channels (e.g., video), and languages.

Methodology

1. Data Collection – The authors gathered a diverse set of spoken utterances from publicly available TV transcripts and podcast recordings, each paired with a clean text transcript.
2. Labeling Semantic Dimensions – Each utterance was annotated for three properties:
   • Sarcasm (yes/no)
   • Emotion (e.g., happy, angry, sad)
   • Questionhood (is it a question?)
3. Model‑Based Feature Extraction –
   • Audio channel: A large speech model (e.g., Whisper) processes the raw waveform and produces high‑dimensional embeddings that capture prosodic patterns.
   • Text channel: A language model (e.g., BERT) encodes the transcript into comparable embeddings.
4. Estimating Mutual Information (MI) – Using a neural estimator (e.g., MINE), the authors compute MI between the embeddings of each channel and the target label. This yields a numeric measure of “how much information about sarcasm/emotion/questionhood is present in audio vs. text.”
5. Comparative Analysis – By contrasting the MI values, they quantify the additional information contributed by prosody beyond what the words already provide.

Results & Findings

• Sarcasm & Emotion: Prosody carries 10–15× more information than the transcript when the listener only has the current sentence. This suggests that pitch contours, timing, and intensity are primary cues for these affective states.
• Questionhood: The audio channel adds only a modest boost, indicating that syntactic cues (e.g., word order, question marks) dominate the detection of questions.
• Context Dependency: The advantage of prosody shrinks when long‑range discourse context is available, aligning with intuition that humans use both channels together in natural conversation.

Practical Implications

• Improved Voice Assistants: Current assistants rely heavily on text transcriptions. Incorporating prosodic embeddings could dramatically boost sarcasm detection and emotional awareness, leading to more natural, empathetic responses.
• Real‑Time Sentiment Monitoring: Call‑center analytics, live streaming moderation, and podcast indexing can benefit from audio‑first models that flag emotional spikes or sarcastic remarks without waiting for transcription.
• Multimodal NLP Pipelines: The MI framework offers a principled way to decide which modality to prioritize for a given downstream task, saving compute by discarding low‑information channels.
• Accessibility Tools: For hearing‑impaired users, enhanced captions that convey prosodic cues (e.g., “[sarcastic tone]”) could be generated automatically using the audio‑derived signals identified here.
• Cross‑Language Transfer: Since prosody is language‑agnostic to a degree, the approach could help bootstrap affective speech detection in low‑resource languages where large text corpora are scarce.

Limitations & Future Work

• Single‑Utterance Focus: The study deliberately excludes broader conversational context, which in real applications is often available and can shift the balance between audio and text.
• Domain Specificity: TV and podcast data are relatively clean and scripted; performance on noisy, spontaneous speech (e.g., meetings) remains untested.
• Model Dependence: MI estimates rely on the quality of the underlying speech and language models; biases or gaps in those models could affect the measured information.
• Scalability of Annotation: Manual labeling of sarcasm and nuanced emotions is costly; future work could explore weak supervision or self‑supervised signals.
• Extension to Additional Channels: The authors propose adding visual cues (facial expressions) and multilingual corpora, which could reveal new interaction patterns between channels.

Bottom line: By quantifying exactly how much “meaning” lives in the melody of speech, this work opens a clear path for developers to build smarter, more emotionally aware voice‑first applications.

Authors

• Aditya Yadavalli
• Tiago Pimentel
• Tamar I Regev
• Ethan Wilcox
• Alex Warstadt

Paper Information

• arXiv ID: 2512.16832v1
• Categories: cs.CL
• Published: December 18, 2025
• PDF: Download PDF