arXiv:2608. 01281v1 Announce Type: cross Abstract: Phoneme-based multilingual automatic speech recognition (ASR) can share acoustic evidence across languages more directly than language-specific subword modeling.
By Saierdaer Yusuyin, Nanling Jiang, Hao Huang, Zhijian Ou
Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representations. We ask whether speaker-linked attributes that...
arXiv:2609.18533v1 Announce Type: new
Abstract: Automatic speech recognition (ASR) systems exhibit unequal error rates across speaker groups, motivating interventions on their internal representation...
By Nicolas Bourrel, Abderrahmane Issam, Gerasimos Spanakis
arXiv:2608.22452v1 Announce Type: new
Abstract: Surprisal, the negative log-probability a language model assigns to a word given its preceding context, reliably predicts adult reading times. Does it...
By Francisco Portillo L\'opez
The paper introduces a method that allows automatic speech recognition systems to learn new words during test time using unlabeled data. It combines a frozen CTC acoustic model for spellings, a frozen language model for detecting out‑of‑vocabulary words, and an adaptation module that expands the vocabulary by learning lexical token representations from CTC-generated candidates. Experiments on LibriSpeech and dysarthric speech data show relative character‑error‑rate reductions of up to 14.97% and 6.67% for recurring OOV words, respectively.
By Mengqi Wang, Mark A. Hasegawa-Johnson, Haolong Zheng, Chang D. Yoo
arXiv:2605. 00025v3 Announce Type: replace-cross Abstract: Speech neuroprosthesis systems decode intended speech from neural activity in the absence of audible output, offering a path to restoring communication for individuals with speech-impairing conditions.
By Yuanhao Chen, Peter Chin