Disfluencies aren’t just noise — they’re part of how we speak.

In our #INTERSPEECH2024 paper, we looked at how Google ASR vs WhisperX handle messy, real-world podcasts (82k+ episodes!):

🎙️ WhisperX → better with “uh/um”
📝 Google ASR → better with self-corrections

Last year at INTERSPEECH 2024, we explored a question that remains relevant: how do ASR systems handle disfluencies in real-world speech?

Stories shape how we think and connect. 📖
But can AI tell a good story?

Our Survey on LLMs for Story Generation (EMNLP Findings 2025) explores:
✨ Coherence
🎛️ Controllability
🎨 Creativity
⚖️ Authenticity

📄 mariateleki.github.io/pdf/A_Survey...

#StoryGeneration #GenerativeAI

🚀 New on arXiv: We introduce DRES, the disfluency removal evaluation suite!

🌟 New on arXiv — we introduce Z-Scores: A Metric for Linguistically Assessing Disfluency Removal 📊🧠

🤔 Traditional F1 scores hide why disfluency removal models succeed or fail.

Speech is messy — and so are recommender systems when they face speech errors.

In our INTERSPEECH 2025 paper, we introduced Syn-WSSE, a psycholinguistically grounded framework for simulating whole-word substitution errors in conversational recommenders (e.g., “I want a horror—comedy movie”).

Messy, real-world speech ≠ clean transcripts.

In our #INTERSPEECH2024 paper, we compared Google ASR vs WhisperX on 82k+ podcasts 🎙️

🌱 WhisperX → better with accurately transcribing “uh/um”
🌱 Google ASR → better with accurately transcribing edited nodes
🌱 Which to use? Depends on your data.

Excited to share our paper A Survey on LLMs for Story Generation! 🎉

We’re also releasing a community resource on GitHub — please feel free to send a pull request as new systems come out.

Paper: tinyurl.com/3jmdkx72
Github: tinyurl.com/2hmrkvrt

#NLP #LLMs #EMNLP2025