Enterprise AI Analysis
Spoken DialogSum: An Emotion-Rich Conversational Dataset for Spoken Dialogue Summarization
Recent progress in Audio-LLMs—such as WavLLM, SALMONN, Qwen-Audio, and LTU-AS—demonstrates the feasibility of directly modeling speech for downstream language tasks. However, existing benchmarks lack the data that links speech, summaries, and paralinguistic cues for emotion-aware or spoken dialogue summarization. Spoken DialogSum addresses this gap by providing a large-scale corpus of 13,460 emotion-diverse dialogues, each paired with both a factual and an emotion-focused summary, along with utterance-level labels for speaker age, gender, and emotion. The dataset is built by transforming DialogSum scripts with Switchboard-style fillers and back-channels, tagging utterances with emotion, pitch, and speaking rate, and synthesizing high-fidelity speech. Baselines show that an Audio-LLM raises emotional-summary ROUGE-L by 28% relative to a cascaded ASR-LLM system, confirming the value of end-to-end speech modeling.
Executive Impact & Key Metrics
Spoken DialogSum provides a groundbreaking resource for developing more emotionally intelligent and contextually aware AI, offering significant advancements in conversational AI applications.
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Total Dialogues
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Total Audio
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Emotion-Rich Summarization ROUGE-L (Audio-LLM)
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Oral Naturalness Score
Deep Analysis & Enterprise Applications
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13,460
Emotion-Diverse Dialogues Generated
Enterprise Process Flow
DialogSum Scripted Dialogues
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Style Transfer (Fillers, Disfluencies)
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Backchannel Insertion
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Realistic Dialogues (with Emotion & Prosodic Labels)
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Speech Synthesis (Conditional TTS)
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Synthetic Spoken Dialogues + Summaries
| Feature | Spoken DialogSum | Competitors (e.g., Switchboard, MELD) |
|---|---|---|
| Audio Duration | 160 hours | 12-260 hours (often less emotion-rich) |
| Emotion Labels | Utterance-level, 8 canonical emotions + Pitch/Rate | Conversation-level or limited scope |
| Summaries | Factual & Emotion-Rich Summaries | Text-only factual or none |
| Full-Duplex | ✓ | ✓ (some) |
| Speaker Attributes | Age, Gender, Pitch, Expressiveness, Speaking Rate | Limited or none |
| Data Origin | Synthetic (LLM-augmented, TTS) | Human-recorded or human-read scripted |
29%
ROUGE-L Improvement (Audio-LLM vs ASR-LLM)
Impact of End-to-End Audio-LLMs for Emotion-Rich Summarization
The study demonstrates that Audio-LLMs significantly outperform cascaded ASR-LLM systems for emotion-rich summarization. By directly processing raw waveforms and integrating paralinguistic cues, Audio-LLMs achieve a 29% relative ROUGE-L improvement in capturing emotional nuances in summaries. This highlights the crucial role of joint semantic and acoustic modeling for tasks requiring deep conversational understanding, beyond just textual content. For enterprises, this means more accurate and nuanced AI for customer service analysis, sentiment tracking, and personalized communication. For instance, an AI reviewing call center interactions could not only identify the topic of conversation but also discern the caller's frustration or satisfaction level, leading to targeted interventions and improved customer experience.
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Estimated Annual Savings
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Reclaimed Annual Employee Hours
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