The paper introduces a personalized Korean visual speech recognition system that uses a video-only Conformer model initialized from English-trained weights, achieving a character error rate (CER) of 9.95–12.19% on the OLKAVS nine-camera corpus and 19.00–21.52% on unseen wording. Individual speaker CER varies widely (1.0–52.2%), with seen wording reducing errors by 7.0–9.0 points and professional or spontaneous speech increasing errors by 8.5–12.7 points. A low‑rank adapter, comprising only 4.6% of the model parameters and trained on 4–29 minutes of a user’s frontal video, reduces high‑error speakers’ CER by 2.13–3.58 points, transfers across all cameras without loss, and retains 85% of full fine‑tuning benefits at 12% of its cost; cameras above the mouth plane add a constant offset of about six CER points that can be mitigated by training on all views.
arXiv:2610.00825v1 Announce Type: new Abstract: Dubbing quality control requires a reference-free judge that can determine whether a candidate text line matches a speaker's visible articulation in bo...
arXiv:2609.14991v1 Announce Type: new Abstract: Open Thai automatic speech recognition (ASR) is dominated by offline, Whisper-based models that read the whole utterance before transcribing, ruling ou...
Open Thai automatic speech recognition (ASR) is dominated by offline, Whisper-based models that read the whole utterance before transcribing, ruling out low-latency uses such as live captioning and vo...
arXiv:2604.19782v2 Announce Type: replace-cross Abstract: Recent advances in large audio language models (LALMs) have enabled multilingual speech understanding. However, benchmarks for evaluating LAL...
arXiv:2609.14542v1 Announce Type: new Abstract: Neyshekar is presented as an open Persian read-speech corpus designed for coverage of both formal and informal language, named entities, and longer utt...
The paper investigates how to close the quality gap in low‑resource text‑to‑speech for Khmer and Korean using the VoxCPM2 model. By training a single low‑rank adaptation (LoRA) adapter on a shared 25.5‑hour corpus, the authors improve Khmer’s mean opinion score from 3.85 to 4.23 with a rank‑64 adapter, while Korean shows no significant gain. The study highlights that adaptation benefits mainly when the base model is weak and that training loss does not always align with human ratings.
arXiv:2603. 04710v2 Announce Type: replace-cross Abstract: Recent advances in automatic speech recognition (ASR) and speech enhancement have strengthened the common belief that cleaner audio should lead to more accurate transcription.
arXiv:2609.21288v1 Announce Type: new Abstract: Surface electromyography (sEMG)-based silent speech interfaces are limited by cross-user variability and calibration burden. We study a limited-data se...
arXiv:2609.10366v1 Announce Type: cross Abstract: While AVSR has achieved sub-1% word error rates on the standard LRS3 benchmark, its reliance on broadcast speech obscures whether this reflects true...
arXiv:2609.10394v1 Announce Type: cross Abstract: Current audio-visual speech recognition (AVSR) benchmarks, like LRS3, rely heavily on clean, scripted and rehearsed speech. They fail to reflect the...
arXiv:2607. 17164v1 Announce Type: new Abstract: Developing Automatic Speech Recognition (ASR) for morphologically rich, low-resource languages such as Assamese is challenging due to insufficient annotated speech data.
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...