2 citations · 3 across the 12 of their papers we have counts for
13 papers
A Cross Community Agenda for Speech AI
Maria Teleki, Kimi Wenzel, Anna Seo Gyeong Choi +20
Speech AI, any AI system that recognizes, transforms, or generates speech, is built and evaluated across two communities with only a small overlap: technical natural language proce…
The Role of Disfluencies in Speech Translation
Maike Züfle, Maria Teleki, Fabian Retkowski +5
Current speech translation systems, including SpeechLLMs, are trained on cleaned text and tend to strip disfluencies like filled pauses and false starts rather than translate them.…
Beyond Single Ground Truth: Reference Monism as Epistemic Injustice in ASR Evaluation
Anna Seo Gyeong Choi, Maria Teleki, James Caverlee +3
Automatic speech recognition (ASR) evaluation compares system output to ground truth transcripts, with Word Error Rate (WER) quantifying the distance between them. But ground truth…
From Seeing it to Experiencing it: Interactive Evaluation of Intersectional Voice Bias in Human-AI Speech Interaction
Shree Harsha Bokkahalli Satish, Maria Teleki, Christoph Minixhofer +3
SpeechLLMs process spoken language directly from audio, but accent and vocal identity cues can lead to biased behaviour. Current bias evaluations often miss how such bias manifests…
The Voice Behind the Words: Quantifying Intersectional Bias in SpeechLLMs
Shree Harsha Bokkahalli Satish, Christoph Minixhofer, Maria Teleki +5
Speech Large Language Models (SpeechLLMs) process spoken input directly, retaining cues such as accent and perceived gender that were previously removed in cascaded pipelines. This…
SocialPulse: An Open-Source Subreddit Sensemaking Toolkit
Stephanie Birkelbach, Maria Teleki, Peter Carragher +3
Understanding how online communities discuss and make sense of complex social issues is a central challenge in social media research, yet existing tools for large-scale discourse a…
PromptHelper: A Prompt Recommender System for Encouraging Creativity in AI Chatbot Interactions
Jason Kim, Maria Teleki, James Caverlee
Prompting is central to interaction with AI systems, yet many users struggle to explore alternative directions, articulate creative intent, or understand how variations in prompts…
CHOIR: Collaborative Harmonization fOr Inference Robustness
Xiangjue Dong, Cong Wang, Maria Teleki +2
Persona-assigned Large Language Models (LLMs) can adopt diverse roles, enabling personalized and context-aware reasoning. However, even minor demographic perturbations in personas,…
Z-Scores: A Metric for Linguistically Assessing Disfluency Removal
Maria Teleki, Sai Janjur, Haoran Liu +10
Evaluating disfluency removal in speech requires more than aggregate token-level scores. Traditional word-based metrics such as precision, recall, and F1 (E-Scores) capture overall…
Conversational Speech Reveals Structural Robustness Failures in SpeechLLM Backbones
Maria Teleki, Sai Janjur, Haoran Liu +11
LLMs serve as the backbone in SpeechLLMs, yet their behavior on spontaneous conversational input remains poorly understood. Conversational speech contains pervasive disfluencies --…
Masculine Defaults via Gendered Discourse in Podcasts and Large Language Models
Maria Teleki, Xiangjue Dong, Haoran Liu +1
Masculine defaults are widely recognized as a significant type of gender bias, but they are often unseen as they are under-researched. Masculine defaults involve three key parts: (…
A Survey on LLM Inference-Time Self-Improvement
Xiangjue Dong, Maria Teleki, James Caverlee
Techniques that enhance inference through increased computation at test-time have recently gained attention. In this survey, we investigate the current state of LLM Inference-Time…
CoPT: Mitigating Bias in Pre-trained Language Models through Counterfactual Contrastive Prompt Tuning
Xiangjue Dong, Ziwei Zhu, Zhuoer Wang +2
Pre-trained Language Models are widely used in many important real-world applications. However, recent studies show that these models can encode social biases from large pre-traini…