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eess.AS2026

Grounding Spoken LLMs in Multi-Speaker Audio via Diarization Conditioning

Alexander Polok, Samuele Cornell, Sathvik Udupa +3

We propose diarization-conditioned spoken language models (SLMs), a strategy for extending SLMs to far-field multi-talker audio. Rather than adapting the decoder via Serialized Out…

eess.AS2026

Endpoint Anticipation for Low-Latency Spoken Dialogue

Sathvik Udupa, Shinji Watanabe, Petr Schwarz +1

While low-latency interaction is critical for spoken dialogue, cascaded architectures are often bottlenecked by reactive turn-completion detection. We propose Endpoint Anticipation…

eess.AS2026

VAANI: Capturing the language landscape for an inclusive digital India

Sujith Pulikodan, Abhayjeet Singh, Agneedh Basu +17

Voice based technologies have the potential to bridge digital accessibility gaps; however, existing datasets fail to capture the linguistic and regional diversity of Indic language…

eess.AS2025

MMAU-Pro: A Challenging and Comprehensive Benchmark for Holistic Evaluation of Audio General Intelligence

Sonal Kumar, Šimon Sedláček, Vaibhavi Lokegaonkar +31

Audio comprehension-including speech, non-speech sounds, and music-is essential for achieving human-level intelligence. Consequently, AI agents must demonstrate holistic audio unde…

eess.AS2025

Discovering phoneme-specific critical articulators through a data-driven approach

Jesuraj Bandekar, Sathvik Udupa, Prasanta Kumar Ghosh

We propose an approach for learning critical articulators for phonemes through a machine learning approach. We formulate the learning with three models trained end to end. First, w…