5 citations · 6 across the 11 of their papers we have counts for
4 papers · 1 filter
ORCA: Open-ended Response Correctness Assessment for Audio Question Answering
Šimon Sedláček, Sara Barahona, Bolaji Yusuf +9
Reliable assessment of the abilities of large audio language models (LALMs) is essential to advancing the state of the art. As benchmarks rapidly evolve to incorporate complex reas…
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…
Streaming Endpointer for Spoken Dialogue using Neural Audio Codecs and Label-Delayed Training
Sathvik Udupa, Shinji Watanabe, Petr Schwarz +1
Accurate, low-latency endpointing is crucial for effective spoken dialogue systems. While traditional endpointers often rely on spectrum-based audio features, this work proposes re…
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…