Tootfinder

🧑🏻‍💻 .NET 10 Preview Now Available on #Azure App Service! :azure: https://azure.github.io/AppService/2025/08/26/dotnet-10-preview-on-App-Service.html…

Enhancing Speech Emotion Recognition Leveraging Aligning Timestamps of ASR Transcripts and Speaker Diarization
Hsuan-Yu Wang, Pei-Ying Lee, Berlin Chen
https://arxiv.org/abs/2507.19356

Enhancing Speech Emotion Recognition Leveraging Aligning Timestamps of ASR Transcripts and Speaker Diarization
In this paper, we investigate the impact of incorporating timestamp-based alignment between Automatic Speech Recognition (ASR) transcripts and Speaker Diarization (SD) outputs on Speech Emotion Recognition (SER) accuracy. Misalignment between these two modalities often reduces the reliability of multimodal emotion recognition systems, particularly in conversational contexts. To address this issue, we introduce an alignment pipeline utilizing pre-trained ASR and speaker diarization models, syste…

Large-scale anisotropies of ultra-high-energy cosmic rays measured at the Pierre Auger Observatory
Marta Bianciotto (for the Pierre Auger Collaboration)
https://arxiv.org/abs/2507.19243

Large-scale anisotropies of ultra-high-energy cosmic rays measured at the Pierre Auger Observatory
Measurements of anisotropies in the arrival directions of ultra-high-energy cosmic rays are crucial to pinpoint their sources, which are yet to be discovered. A dipolar anisotropy in right ascension above 8~EeV has been detected by the Pierre Auger Observatory with a significance of $6.8 σ$. The direction of the dipole suggests an extragalactic origin of ultra-high-energy cosmic rays above those energies. In this contribution, we provide an overview of the studies on large-scale anisotropies i…

Distance-informed Neural Processes
Aishwarya Venkataramanan, Joachim Denzler
https://arxiv.org/abs/2508.18903 https://arxiv.org/pdf/2508.18903

Distance-informed Neural Processes
We propose the Distance-informed Neural Process (DNP), a novel variant of Neural Processes that improves uncertainty estimation by combining global and distance-aware local latent structures. Standard Neural Processes (NPs) often rely on a global latent variable and struggle with uncertainty calibration and capturing local data dependencies. DNP addresses these limitations by introducing a global latent variable to model task-level variations and a local latent variable to capture input similar…

DESAMO: A Device for Elder-Friendly Smart Homes Powered by Embedded LLM with Audio Modality
Youngwon Choi, Donghyuk Jung, Hwayeon Kim
https://arxiv.org/abs/2508.18918 https://…

DESAMO: A Device for Elder-Friendly Smart Homes Powered by Embedded LLM with Audio Modality
We present DESAMO, an on-device smart home system for elder-friendly use powered by Audio LLM, that supports natural and private interactions. While conventional voice assistants rely on ASR-based pipelines or ASR-LLM cascades, often struggling with the unclear speech common among elderly users and unable to handle non-speech audio, DESAMO leverages an Audio LLM to process raw audio input directly, enabling a robust understanding of user intent and critical events, such as falls or calls for he…

Exploring the Use of LLMs for Requirements Specification in an IT Consulting Company
Liliana Pasquale, Azzurra Ragone, Emanuele Piemontese, Armin Amiri Darban
https://arxiv.org/abs/2507.19113

Exploring the Use of LLMs for Requirements Specification in an IT Consulting Company
In practice, requirements specification remains a critical challenge. The knowledge necessary to generate a specification can often be fragmented across diverse sources (e.g., meeting minutes, emails, and high-level product descriptions), making the process cumbersome and time-consuming. In this paper, we report our experience using large language models (LLMs) in an IT consulting company to automate the requirements specification process. In this company, requirements are specified using a Fun…

Constraining the origin of the highest-energy cosmic-ray events detected by the Pierre Auger Observatory: a three-dimensional approach
Marta Bianciotto (for the Pierre Auger Collaboration)
https://arxiv.org/abs/2507.19216

Constraining the origin of the highest-energy cosmic-ray events detected by the Pierre Auger Observatory: a three-dimensional approach
Unveiling the sources of ultra-high-energy cosmic rays remains one of the main challenges of high-energy astrophysics. Measurements of anisotropies in their arrival directions are key to identifying their sources, yet magnetic deflections obscure direct associations. In this work, we reconstruct the sky regions of possible origin of the highest-energy cosmic-ray events detected by the Pierre Auger Observatory by tracing their trajectories through Galactic magnetic fields using up-to-date models…

Towards stable AI systems for Evaluating Arabic Pronunciations
Hadi Zaatiti, Hatem Hajri, Osama Abdullah, Nader Masmoudi
https://arxiv.org/abs/2508.19587 https://

Towards stable AI systems for Evaluating Arabic Pronunciations
Modern Arabic ASR systems such as wav2vec 2.0 excel at word- and sentence-level transcription, yet struggle to classify isolated letters. In this study, we show that this phoneme-level task, crucial for language learning, speech therapy, and phonetic research, is challenging because isolated letters lack co-articulatory cues, provide no lexical context, and last only a few hundred milliseconds. Recogniser systems must therefore rely solely on variable acoustic cues, a difficulty heightened by A…

It's All About In-Context Learning! Teaching Extremely Low-Resource Languages to LLMs
Yue Li, Zhixue Zhao, Carolina Scarton
https://arxiv.org/abs/2508.19089 https://

It's All About In-Context Learning! Teaching Extremely Low-Resource Languages to LLMs
Extremely low-resource languages, especially those written in rare scripts, as shown in Figure 1, remain largely unsupported by large language models (LLMs). This is due in part to compounding factors such as the lack of training data. This paper delivers the first comprehensive analysis of whether LLMs can acquire such languages purely via in-context learning (ICL), with or without auxiliary alignment signals, and how these methods compare to parameter-efficient fine-tuning (PEFT). We systemat…

TokenVerse : Towards Flexible Multitask Learning with Dynamic Task Activation
Shashi Kumar, Srikanth Madikeri, Esa\'u Villatoro-Tello, Sergio Burdisso, Pradeep Rangappa, Andr\'es Carofilis, Petr Motlicek, Karthik Pandia, Shankar Venkatesan, Kadri Hacio\u{g}lu, Andreas Stolcke
https://arxiv.org/abs/2508.19856

TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation
Token-based multitasking frameworks like TokenVerse require all training utterances to have labels for all tasks, hindering their ability to leverage partially annotated datasets and scale effectively. We propose TokenVerse++, which introduces learnable vectors in the acoustic embedding space of the XLSR-Transducer ASR model for dynamic task activation. This core mechanism enables training with utterances labeled for only a subset of tasks, a key advantage over TokenVerse. We demonstrate this b…