📄 buff.ly/S0DSZzt
⚽️ Xiangjue Dong (1st author), Cong Wang, Millenium Bismay, and James Caverlee
#NLP #NLPResearch #LLMs #GenAI #AI

🌟 To me, this work is super exciting because we take a totally different perspective: we show that ⬆️ diverse perspectives, ⬆️ system performance, so ⬆️ $$$ for a company! With this work, we argue that <<< 🚨 diverse perspectives are absolutely necessary >>> from an economic standpoint.

You always hear about the "bias-accuracy tradeoff," meaning that ⬇️ model bias, ⬇️ system performance, so ⬇️ $$$ for a company. So much of the conversation around bias and diversity has focused on how to incentivize companies to debias their models (e.g., through new legislation).

Choosing an ASR system isn’t one-size-fits-all — it depends on the disfluencies in your domain.

📄 www.isca-archive.org/interspeech_...

I’m working on methods & evaluation frameworks for conversational AI that are:
✅ Robust to disfluencies
✅ Reliable in noisy, real-world conditions
✅ Generalizable across contexts

If conversational AI is going to truly work for everyone, it must be built for human speech as it is.

These insights still apply to anyone working on conversational AI, spoken summarization, or voice-driven interfaces today.

📄 Read more: www.isca-archive.org/interspeech_...

#SpeechProcessing #ConversationalAI #VoiceAI #Disfluency #SpokenLanguage
#INTERSPEECH

We compared two systems on 82,000+ podcast episodes. We found:
👉 WhisperX better captures interjections like “uh” and “um”
👉 Google ASR better captures edited nodes (e.g., “let’s go to Target--Walmart”)

🌟 The type of disfluency matters when choosing an ASR system

📄 Paper: mariateleki.github.io/pdf/A_Survey... | 💻 GitHub: github.com/mariateleki/...

#StoryGeneration #GenerativeAI #NLProc

#arxiv #speechAI #LLM

🧭 9 actionable recommendations for deployment -- some are surprising! 😱
📄 Paper: arxiv.org/pdf/2509.20321
💻 Code: github.com/mariateleki/...

🔬 DRES provides the first controlled benchmark for evaluating LLMs on disfluency removal.
✅ Controlled evaluation on gold transcripts (no ASR noise) sets an upper bound
📊 Systematic comparison across open & proprietary LLMs
🧪 First taxonomy of LLM error modes

🔎 Z-Scores reveal model weaknesses by disfluency type — EDITED, INTJ, and PRN — providing diagnostic insights that guide targeted improvements.

📄 Paper: arxiv.org/abs/2509.20319
💻 Code: github.com/mariateleki/...