RAIL is introduced, a human-centric benchmark grounded in the Cattell--Horn--Carroll (CHC) framework, which formalises auditory cognition into five core capabilities, and develops them into structured evaluation tasks with principled data curation and human-aligned evaluation protocols.
Hong-Yu Jin, Siyi Wang, Yang Xiao et al.· arXiv.org· 3 citations
This work introduces DuplexAct, a benchmark for evaluating interaction action taking in E2E FD-SLMs and defines a three-level action taxonomy spanning eight interaction actions and 24 corresponding interaction conditions, and develops action-specific evaluation metrics for both.
Jia-Heng Dong, Shi-Hong Tan, Gong-Ping Huang et al.· 0 citations
It is argued that principled memory evaluation requires jointly characterizing the acoustic evidence to be retained and the operations applied to it, and introduced a taxonomy along these two axes, which aims to provide a foundation to measure and drive progress on the full scope of spoken conversational memory.
Xiao Yang, Vidhyasaharan Sethu, Eun-Jung Holden et al.· 0 citations
This study investigates exemplar-free analytic continual learning as a principled alternative, in which a linear classifier is updated in closed form without storing historical recordings or performing incremental back-propagation, and previously learned weights remain intact by construction.
Easper is presented, an open-source, no-code workflow enabling linguists to iteratively fine-tune ASR models via cloud resources directly from ELAN annotations, demonstrating that prioritising lexically rich narratives and increasing acoustic-phonetic repetition, even in noisy environments, leads to faster improvements...
Aso Mahmudi, Ting Dang, Ekaterina Vylomova et al.· 0 citations
MADBench is introduced, the first benchmark that treats speech and environmental audio as distinct acoustic components, enabling component-aware evaluation of audio deepfake detection across independently manipulated forgery sources and establishes a rigorous foundation for future research into robust, component-aware...
Yan-Qiu Li, Yang Xiao, Jisheng Bai et al.· 0 citations
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