The paper presents an interpretable, fair, and accurately benchmarked automated system for assessing second‑language English speaking. Using a hybrid of feature‑based speech‑timing metrics and a large language model (LLM) fluency judgment, the system achieves a Spearman correlation of 0.818 with the ICNALE Global Rating Archive, outperforming 81 % of trained human raters. A controlled study shows that encoding pauses into the LLM prompt does not meaningfully affect fluency scores, indicating that the system’s fluency signal derives from measurable speech‑timing features.
By Eichi Uehara
arXiv:2608.03854v4 Announce Type: replace
Abstract: Quantized large language models can run on consumer hardware, which motivates interest in on-premises processing of sensitive data. The reliability...
By Anton Rasmussen, Hong Qin
The study examined whether providing full prompt-level context to a large multimodal model would improve speech transcription accuracy on a production oral‑history corpus. Using a preregistered within‑item paired ablation, the authors found that adding context did not produce a detectable change in side‑level word error rate (WER) for either gpt‑4o‑transcribe or gemini‑2.5‑flash. The results suggest that context alone may not be sufficient to enhance aggregate transcription accuracy, and that finer‑grained, sequence‑aligned metrics are needed to evaluate such mechanisms.
By Theodore O. Cochran, Stephanie Dodson, Keith Nore
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:2605. 00865v2 Announce Type: replace-cross Abstract: We tested whether auditory-evoked EEG supports subject-independent five-vowel perception decoding when trial identity, model identity, prediction provenance, and participant-level inference are controlled within a single benchmark.
By Xiaoyang Li, Zeyan Tao
The 2026 PNPL Competition builds on the 2025 PNPL effort by expanding the LibriBrain dataset to 32 new subjects and more within‑subject data, creating LibriBrain100. It introduces two tracks: a Deep track for high‑performance within‑subject word classification and a Broad track that tests cross‑subject generalisation with progressively less subject‑specific fine‑tuning data, down to 10 minutes. The competition aims to advance non‑invasive brain‑computer interfaces toward practical, clinically feasible communication restoration for people with profound paralysis.
By Francesco Mantegna, Gereon Elvers, Dulhan Jayalath, Gilad Landau, Tasha Kim, Miran \"Ozdogan, Luisa Kurth, Teyun Kwon, SungJun Cho, Benjamin Ballyk, Alex Fung, Anna Greer, Pratik Somaiya, Christian Herff, Yorguin Mantilla Ramos, Hamza Abdelhedi, Karim Jerbi, Greg Farquhar, Brendan Shillingford, Mark Woolrich, Oiwi Parker Jones
arXiv:2609.17548v1 Announce Type: new
Abstract: Myovox, from myo (muscle) and vox (voice), decodes open-vocabulary English text from 31-channel surface electromyography (sEMG) recorded from the muscl...
By Varshith Madishetty
arXiv:2606. 19797v1 Announce Type: cross Abstract: Dysarthric speech recognition is crucial for facilitating effective communication among individuals with dysarthria.
By Paban Sapkota, Hemant Kumar Kathania, Sudarsana Reddy Kadiri, Shrikanth Narayanan
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:2609.14231v1 Announce Type: cross
Abstract: Controllable synthesis of nonverbal vocalizations (NVVs) is es- sential for natural and expressive speech, but remains challeng- ing due to their aco...
By Ziyu Zhang, Yun Chen, Taihui Wang, Hanzhao Li, Qicong Xie, Rilin Chen, Zhixian Zhao, Lei Xie
The paper presents a new evaluation protocol for post‑training quantization of speech language models that separates lexical output, transcript‑insufficient endpoints, and packed implementations. In a Qwen2‑Audio case study, a 6‑bit allocation selected for translation improves chrF scores but degrades emotion recognition, while uniform and front‑layer controls perform better on emotion tasks. Similar patterns hold at 7 bits, and a 4‑bit study shows consistent emotion deficits across all low‑bit allocations, with no advantage for the selected scheme. The study highlights a precision‑dependent mismatch between lexical output, waveform‑dependent behavior, and nominal precision, without claiming a general failure of low‑bit models or a deployment benefit for the selected allocation.
By Mengzhe Geng, Jinxi Jin, Junhao Xu
The study evaluates seven parameter‑efficient fine‑tuning (PEFT) methods—LoRA, QLoRA, AdaLoRA, DoRA, LoHA, VeRA, and VB‑LoRA—on two ASR back‑ends (Whisper‑large‑v3 and Qwen3‑ASR‑1.7B) for a single post‑stroke Hungarian male speaker with severe dysarthria. Attention‑projection adapters consistently lower character error rates (CER) on both models, with LoRA emerging as the simplest and most effective choice; QLoRA performs worse and offers no memory advantage at this scale. Full fine‑tuning yields the lowest CER, but a 115 MB LoRA that also adapts feed‑forward blocks achieves comparable accuracy with only 3.7 % of the per‑patient storage, and a 5‑minute enrollment audio captures nearly half of the zero‑shot‑to‑30‑minute CER improvement.
whyItMatters":"The paper demonstrates that lightweight PEFT adapters can substantially improve dysarthric ASR performance while keeping storage and computational costs low, offering a practical path for personalized speech recognition in clinical settings."
By Bernard Muller, L\'aszl\'o T\'oth, LaVonne Roberts