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From the 1 of 6 linked papers with an AI index.

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6 papers

cs.SD2026

Cocktail-Talker: Multi-Speaker Dialog Modeling in Noisy Social Environments with Turn Action GRPO

Xilin Jiang, Riki Shimizu, Sukru Samet Dindar +3

The paper presents Cocktail-Talker, a speech‑language model framework that lets a spoken assistant decide whether to respond, listen, or ignore in multi‑speaker, noisy social conve…

cs.SD2026

Sympatheia: Emotionally Adaptive Voice Assistant with Continuous Affect Conditioning

Sukru Samet Dindar, Riki Shimizu, Xilin Jiang +1

Empathetic spoken dialogue systems must infer a user's emotional state to respond appropriately, yet everyday speech often carries weak, neutral, or ambiguous affective cues. To ad…

cs.SD2026

AVMeme Exam: A Multimodal Multilingual Multicultural Benchmark for LLMs' Contextual and Cultural Knowledge and Thinking

Xilin Jiang, Qiaolin Wang, Junkai Wu +30

Internet audio-visual clips convey meaning through time-varying sound and motion, which extend beyond what text alone can represent. To examine whether AI models can understand suc…

eess.AS2025

MeanFlow-TSE: One-Step Generative Target Speaker Extraction with Mean Flow

Riki Shimizu, Xilin Jiang, Nima Mesgarani

Target speaker extraction (TSE) aims to isolate a desired speaker's voice from a multi-speaker mixture using auxiliary information such as a reference utterance. Although recent ad…

q-bio.NC2025

Interpretable Embeddings of Speech Enhance and Explain Brain Encoding Performance of Audio Models

Riki Shimizu, Richard J. Antonello, Chandan Singh +1

Speech foundation models (SFMs) are increasingly hailed as powerful computational models of human speech perception. However, since their representations are inherently black-box,…

cs.CL2025

XCOMPS: A Multilingual Benchmark of Conceptual Minimal Pairs

Linyang He, Ercong Nie, Sukru Samet Dindar +10

We introduce XCOMPS in this work, a multilingual conceptual minimal pair dataset covering 17 languages. Using this dataset, we evaluate LLMs' multilingual conceptual understanding…