arXiv:2608.22196v1 Announce Type: cross
Abstract: While cascaded multi-talker ASR (MT-ASR) leverages state-of-the-art foundation models, its performance is often capped by speaker leakage during sepa...
By Hermann Yepdjio Nkouanga, Minwei Luo, Maggie Wigness, Suresh Singh
The paper introduces a new target‑speaker unlearning task for automatic speech recognition (TSU‑ASR) that allows certain speakers to opt out of transcription while still indicating their presence. A lightweight Enrollment‑Conditioned Gating (ECG) module is added to a frozen dual‑stream speech LLM, enabling dynamic unlearning of new opt‑out speakers during inference. Experiments on AMI and AliMeeting datasets show significant drops in transcription accuracy for opt‑out speakers while preserving performance for retained speakers.
By Bo Su, Yueru Yan, Thai Le
AVERT is a method for spoken dialogue state tracking that improves upon a per-turn text editor by incorporating an audio-conditioned verifier to score candidate slot values. It addresses three types of recoverable errors—inconsistent values across turns, omitted slots, and values unsupported by audio—using three specialized operators: vote, add, and swap, each limited to relevant slots. On the SpokenWOZ dataset, AVERT achieves a joint goal accuracy of 40.13, surpassing both a base speech-LLM (33.04) and a text editor (38.34) without retraining, and matching the performance of a larger end‑to‑end system that processes the full spoken history.
By Chunggi Lee, Hanspeter Pfister
arXiv:2609.09889v1 Announce Type: new
Abstract: Automatic Speech Recognition (ASR) technology is fundamental to customer service automation and large-scale transcription. However, even advanced ASR m...
By Yonghyun Jun, Jimin Lee, Hwan Chang, Dongho Shin, Seolah Kim, Hwanhee Lee
The paper introduces ASCIL, a post‑ASR correction framework that re‑evaluates wake‑up intent by combining acoustic embeddings, linguistic cues, device context, and past misclassifications. ASCIL interprets both implicit (hesitation, disengagement, silence) and explicit (cancellation, repetition) signals as noisy indicators of misclassification, enabling online pattern updates without manual annotation. On a proprietary dataset of 3,667 interactions, ASCIL reduces errors by up to 54.27% relative on a session‑disjoint subset and 24.39% at a 0.90 threshold, while adding less than 60 ms of latency and improving intentional acceptance rates.
By Preeti Saraswat, Divya Neelagiri, Anil Yadav
The paper introduces a cause-aware error recovery framework for cascaded Automatic Speech Recognition – Large Language Model (ASR‑LLM) pipelines in Spoken Dialogue Systems. It replaces simple ASR confidence filtering with precision‑focused detectors that use deep ASR latent representations to classify token‑level errors into perception, comprehension, and deletion failures. This fine‑grained diagnosis enables the LLM to execute targeted, multi‑turn clarification strategies, leading to a more than two‑fold increase in recall on domain‑shift errors and significant reductions in word error rate and downstream task errors across varied accents, distortions, and domains.
By Yizhou Peng, Ziyang Ma, Changsong Liu, Yi-Wen Chao, Xie Chen, Eng Siong Chng