Tootfinder

PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation
Jiajun He, Naoki Sawada, Koichi Miyazaki, Tomoki Toda
https://arxiv.org/abs/2509.04357 https…

PARCO: Phoneme-Augmented Robust Contextual ASR via Contrastive Entity Disambiguation
Automatic speech recognition (ASR) systems struggle with domain-specific named entities, especially homophones. Contextual ASR improves recognition but often fails to capture fine-grained phoneme variations due to limited entity diversity. Moreover, prior methods treat entities as independent tokens, leading to incomplete multi-token biasing. To address these issues, we propose Phoneme-Augmented Robust Contextual ASR via COntrastive entity disambiguation (PARCO), which integrates phoneme-aware …

Contextualized Token Discrimination for Speech Search Query Correction
Junyu Lu, Di Jiang, Mengze Hong, Victor Junqiu Wei, Qintian Guo, Zhiyang Su
https://arxiv.org/abs/2509.04393

Contextualized Token Discrimination for Speech Search Query Correction
Query spelling correction is an important function of modern search engines since it effectively helps users express their intentions clearly. With the growing popularity of speech search driven by Automated Speech Recognition (ASR) systems, this paper introduces a novel method named Contextualized Token Discrimination (CTD) to conduct effective speech query correction. In CTD, we first employ BERT to generate token-level contextualized representations and then construct a composition layer to …

From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding
Xiangfeng Wang, Xiao Li, Yadong Wei, Xueyu Song, Yang Song, Xiaoqiang Xia, Fangrui Zeng, Zaiyi Chen, Liu Liu, Gu Xu, Tong Xu
https://arxiv.org/abs/2507.02790

From Long Videos to Engaging Clips: A Human-Inspired Video Editing Framework with Multimodal Narrative Understanding
The rapid growth of online video content, especially on short video platforms, has created a growing demand for efficient video editing techniques that can condense long-form videos into concise and engaging clips. Existing automatic editing methods predominantly rely on textual cues from ASR transcripts and end-to-end segment selection, often neglecting the rich visual context and leading to incoherent outputs. In this paper, we propose a human-inspired automatic video editing framework (HIVE)…

Using surface plasmons to detect spin inertia
H. Y. Yuan
https://arxiv.org/abs/2508.01627 https://arxiv.org/pdf/2508.01627…

Using surface plasmons to detect spin inertia
Recent experiments demonstrate that spin dynamics may acquire an inertial effect in a few metallic magnets, deviating from the traditional inertia-free dynamics. It remains an open question to ascertain the physical mechanisms and universality of the spin inertia across diverse magnetic systems. Here, we show that spin inertia generates nutation spin waves in the terahertz regime, which can hybridize with the surface plasmons in two-dimensional (2D) conducting materials such as graphene. By exc…

Sources: Coinbase is in advanced talks to acquire CoinDCX, potentially valuing the Indian crypto exchange at less than $1B, below its 2022 $2.15B valuation (Pankaj Mishra/Livemint)
https://www.livemint.com/companies/news/co

Temporal Exposure Dependence Bias in Vaccine Efficacy Trials
Hiroyasu Ando, A. James O'Malley, Akihiro Nishi
https://arxiv.org/abs/2509.03476 https://a…

Temporal Exposure Dependence Bias in Vaccine Efficacy Trials
Using time-to-event methods such as Cox proportional hazards models, it is well established that unmeasured heterogeneity in exposure or infection risk can lead to downward bias in point estimates of the per-contact vaccine efficacy (VE) in infectious disease trials. In this study, we explore an unreported source of bias-arising from temporally correlated exposure status-that is typically unmeasured and overlooked in standard analyses. Although this form of bias can plausibly affect a wide rang…

Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla
Md Sazzadul Islam Ridoy, Sumi Akter, Md. Aminur Rahman
https://arxiv.org/abs/2507.01931

Adaptability of ASR Models on Low-Resource Language: A Comparative Study of Whisper and Wav2Vec-BERT on Bangla
In recent years, neural models trained on large multilingual text and speech datasets have shown great potential for supporting low-resource languages. This study investigates the performances of two state-of-the-art Automatic Speech Recognition (ASR) models, OpenAI's Whisper (Small & Large-V2) and Facebook's Wav2Vec-BERT on Bangla, a low-resource language. We have conducted experiments using two publicly available datasets: Mozilla Common Voice-17 and OpenSLR to evaluate model performances. Th…

US, Germany agree to deliver 5 Patriot air defense systems to Ukraine: https://benborges.xyz/2025/07/21/us-germany-agree-to-deliver.html

Leveraging In-Context Learning for Political Bias Testing of LLMs
Patrick Haller, Jannis Vamvas, Rico Sennrich, Lena A. J\"ager
https://arxiv.org/abs/2506.22232

Leveraging In-Context Learning for Political Bias Testing of LLMs
A growing body of work has been querying LLMs with political questions to evaluate their potential biases. However, this probing method has limited stability, making comparisons between models unreliable. In this paper, we argue that LLMs need more context. We propose a new probing task, Questionnaire Modeling (QM), that uses human survey data as in-context examples. We show that QM improves the stability of question-based bias evaluation, and demonstrate that it may be used to compare instruct…

Zero-shot Context Biasing with Trie-based Decoding using Synthetic Multi-Pronunciation
Changsong Liu, Yizhou Peng, Eng Siong Chng
https://arxiv.org/abs/2508.17796 https://

Zero-shot Context Biasing with Trie-based Decoding using Synthetic Multi-Pronunciation
Contextual automatic speech recognition (ASR) systems allow for recognizing out-of-vocabulary (OOV) words, such as named entities or rare words. However, it remains challenging due to limited training data and ambiguous or inconsistent pronunciations. In this paper, we propose a synthesis-driven multi-pronunciation contextual biasing method that performs zero-shot contextual ASR on a pretrained Whisper model. Specifically, we leverage text-to-speech (TTS) systems to synthesize diverse speech sa…