Tootfinder

Analysis of Domain Shift across ASR Architectures via TTS-Enabled Separation of Target Domain and Acoustic Conditions
Tina Raissi, Nick Rossenbach, Ralf Schl\"uter
https://arxiv.org/abs/2508.09868

Analysis of Domain Shift across ASR Architectures via TTS-Enabled Separation of Target Domain and Acoustic Conditions
We analyze automatic speech recognition (ASR) modeling choices under domain mismatch, comparing classic modular and novel sequence-to-sequence (seq2seq) architectures. Across the different ASR architectures, we examine a spectrum of modeling choices, including label units, context length, and topology. To isolate language domain effects from acoustic variation, we synthesize target domain audio using a text-to-speech system trained on LibriSpeech. We incorporate target domain n-gram and neural …

Learning-To-Measure: In-context Active Feature Acquisition
Yuta Kobayashi, Zilin Jing, Jiayu Yao, Hongseok Namkoong, Shalmali Joshi
https://arxiv.org/abs/2510.12624 https://

Learning-To-Measure: In-context Active Feature Acquisition
Active feature acquisition (AFA) is a sequential decision-making problem where the goal is to improve model performance for test instances by adaptively selecting which features to acquire. In practice, AFA methods often learn from retrospective data with systematic missingness in the features and limited task-specific labels. Most prior work addresses acquisition for a single predetermined task, limiting scalability. To address this limitation, we formalize the meta-AFA problem, where the goal…

Do Railway Commuters Exhibit Consistent Route Choice Rationality Across Different Contexts and Time? Evidence from Tokyo metropolitan Commutes
Yixuan Y Zheng, Hideki Takayasu, Misako Takayasu
https://arxiv.org/abs/2510.12381

Do Railway Commuters Exhibit Consistent Route Choice Rationality Across Different Contexts and Time? Evidence from Tokyo metropolitan Commutes
Recent advances in data collection and technology enable a deeper understanding of complex urban commuting, yet few studies have rigorously analyzed the temporal stability and Origin-Destination (OD) heterogeneity of route choice. To address this, we analyze one year of smartphone position data from over one million users in the Tokyo metropolitan area to extract high-resolution commuting trajectories. Our methodology is twofold: First, we develop algorithms to process raw position data, accura…

Uma sondagem de mais de 35 mil pessoas, iniciada hš quase 3 anos, terminou agora. Pergunto a mim mesmo que percentagem destas contas continua ativa.
https://fosstodon.org/@MattTheQuick/109321444143425443
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Exploring Cross-Utterance Speech Contexts for Conformer-Transducer Speech Recognition Systems
Mingyu Cui, Mengzhe Geng, Jiajun Deng, Chengxi Deng, Jiawen Kang, Shujie Hu, Guinan Li, Tianzi Wang, Zhaoqing Li, Xie Chen, Xunying Liu
https://arxiv.org/abs/2508.10456

Exploring Cross-Utterance Speech Contexts for Conformer-Transducer Speech Recognition Systems
This paper investigates four types of cross-utterance speech contexts modeling approaches for streaming and non-streaming Conformer-Transformer (C-T) ASR systems: i) input audio feature concatenation; ii) cross-utterance Encoder embedding concatenation; iii) cross-utterance Encoder embedding pooling projection; or iv) a novel chunk-based approach applied to C-T models for the first time. An efficient batch-training scheme is proposed for contextual C-Ts that uses spliced speech utterances withi…

Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function
Chin Yuen Kwok, Jia Qi Yip, Eng Siong Chng
https://arxiv.org/abs/2509.09197 ht…

Improving Synthetic Data Training for Contextual Biasing Models with a Keyword-Aware Cost Function
Rare word recognition can be improved by adapting ASR models to synthetic data that includes these words. Further improvements can be achieved through contextual biasing, which trains and adds a biasing module into the model architecture to prioritize rare words. While training the module on synthetic rare word data is more effective than using non-rare-word data, it can lead to overfitting due to artifacts in the synthetic audio. To address this, we enhance the TCPGen-based contextual biasing …

How Surface Make-Up and Receding Electrokinetics Determine the Sign and Magnitude of Electrification at Water-Hydrophobe Interfaces?
Yinfeng Xu, Himanshu Mishra
https://arxiv.org/abs/2509.09930

How Surface Make-Up and Receding Electrokinetics Determine the Sign and Magnitude of Electrification at Water-Hydrophobe Interfaces?
It has been widely reported that as water contacts hydrophobic materials such as air or hydrocarbons (liquid or solid), the interfaces acquire a negative charge. It is not entirely clear whether this occurs due to the nature of water, or the hydrophobe, or purely the interface. Here, we probe the effects of surface chemistry and the speed of liquid-solid contact formation and separation on electrification. Glass capillaries grafted with mixed self-assembled monolayers of octadecyltrichlorosilan…

Agoran: An Agentic Open Marketplace for 6G RAN Automation
Ilias Chatzistefanidis, Navid Nikaein, Andrea Leone, Ali Maatouk, Leandros Tassioulas, Roberto Morabito, Ioannis Pitsiorlas, Marios Kountouris
https://arxiv.org/abs/2508.09159

Agoran: An Agentic Open Marketplace for 6G RAN Automation
Next-generation mobile networks must reconcile the often-conflicting goals of multiple service owners. However, today's network slice controllers remain rigid, policy-bound, and unaware of the business context. We introduce Agoran Service and Resource Broker (SRB), an agentic marketplace that brings stakeholders directly into the operational loop. Inspired by the ancient Greek agora, Agoran distributes authority across three autonomous AI branches: a Legislative branch that answers compliance q…

Whisfusion: Parallel ASR Decoding via a Diffusion Transformer
Taeyoun Kwon, Junhyuk Ahn, Taegeun Yun, Heeju Jwa, Yoonchae Choi, Siwon Park, Nam-Joon Kim, Jangchan Kim, Hyun Gon Ryu, Hyuk-Jae Lee
https://arxiv.org/abs/2508.07048

Whisfusion: Parallel ASR Decoding via a Diffusion Transformer
Fast Automatic Speech Recognition (ASR) is critical for latency-sensitive applications such as real-time captioning and meeting transcription. However, truly parallel ASR decoding remains challenging due to the sequential nature of autoregressive (AR) decoders and the context limitations of non-autoregressive (NAR) methods. While modern ASR encoders can process up to 30 seconds of audio at once, AR decoders still generate tokens sequentially, creating a latency bottleneck. We propose Whisfusion…

TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree
Andrei Andrusenko, Vladimir Bataev, Lilit Grigoryan, Vitaly Lavrukhin, Boris Ginsburg
https://arxiv.org/abs/2508.07014

TurboBias: Universal ASR Context-Biasing powered by GPU-accelerated Phrase-Boosting Tree
Recognizing specific key phrases is an essential task for contextualized Automatic Speech Recognition (ASR). However, most existing context-biasing approaches have limitations associated with the necessity of additional model training, significantly slow down the decoding process, or constrain the choice of the ASR system type. This paper proposes a universal ASR context-biasing framework that supports all major types: CTC, Transducers, and Attention Encoder-Decoder models. The framework is bas…