Tootfinder

BoSS: Beyond-Semantic Speech
Qing Wang, Zehan Li, Hang Lv, Hongjie Chen, Yaodong Song, Jian Kang, Jie Lian, Jie Li, Yongxiang Li, Zhongjiang He, Xuelong Li
https://arxiv.org/abs/2507.17563

BoSS: Beyond-Semantic Speech
Human communication involves more than explicit semantics, with implicit signals and contextual cues playing a critical role in shaping meaning. However, modern speech technologies, such as Automatic Speech Recognition (ASR) and Text-to-Speech (TTS) often fail to capture these beyond-semantic dimensions. To better characterize and benchmark the progression of speech intelligence, we introduce Spoken Interaction System Capability Levels (L1-L5), a hierarchical framework illustrated the evolution…

Nachgebauter Schopska-Salat (immerhin tatsächlich originaler Käse) und Slivovitz, ein bisschen als Abschied vom Lieblingsonkel
#AusGründen

Constraining the Extragalactic Magnetic Field: Auger Data Meet UHECR Propagation Modeling
Ala'a AL-Zetoun, Arjen van Vliet, Andrew M. Taylor, Walter Winter
https://arxiv.org/abs/2506.16169

Constraining the Extragalactic Magnetic Field: Auger Data Meet UHECR Propagation Modeling
Recent analyses from the Pierre Auger Collaboration suggest correlations between the arrival directions of Ultra-High-Energy Cosmic Rays (UHECRs) and catalogs of starburst galaxies (SGBs) and jetted active galactic nuclei (AGNs). We revisit these analyses using the same methodology as \auger , but explicitly incorporating UHECR deflections in turbulent extragalactic magnetic fields (EGMFs). We demonstrate that while for SBGs the same sources as for the generic \auger\ analysis dominate the cata…

An approach to measuring the performance of Automatic Speech Recognition (ASR) models in the context of Large Language Model (LLM) powered applications
Sujith Pulikodan, Sahapthan K, Prasanta Kumar Ghosh, Visruth Sanka, Nihar Desai
https://arxiv.org/abs/2507.16456

An approach to measuring the performance of Automatic Speech Recognition (ASR) models in the context of Large Language Model (LLM) powered applications
Automatic Speech Recognition (ASR) plays a crucial role in human-machine interaction and serves as an interface for a wide range of applications. Traditionally, ASR performance has been evaluated using Word Error Rate (WER), a metric that quantifies the number of insertions, deletions, and substitutions in the generated transcriptions. However, with the increasing adoption of large and powerful Large Language Models (LLMs) as the core processing component in various applications, the significan…

Application of Whisper in Clinical Practice: the Post-Stroke Speech Assessment during a Naming Task
Milena Davudova, Ziyuan Cai, Valentina Giunchiglia, Dragos C. Gruia, Giulia Sanguedolce, Adam Hampshire, Fatemeh Geranmayeh
https://arxiv.org/abs/2507.17326

Application of Whisper in Clinical Practice: the Post-Stroke Speech Assessment during a Naming Task
Detailed assessment of language impairment following stroke remains a cognitively complex and clinician-intensive task, limiting timely and scalable diagnosis. Automatic Speech Recognition (ASR) foundation models offer a promising pathway to augment human evaluation through intelligent systems, but their effectiveness in the context of speech and language impairment remains uncertain. In this study, we evaluate whether Whisper, a state-of-the-art ASR foundation model, can be applied to transcri…

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
John Alderete, Macarious Kin Fung Hui, Aanchan Mohan
https://arxiv.org/abs/2508.13060

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
The Simon Fraser University Speech Error Database (SFUSED) is a public data collection developed for linguistic and psycholinguistic research. Here we demonstrate how its design and annotations can be used to test and evaluate speech recognition models. The database comprises systematically annotated speech errors from spontaneous English speech, with each error tagged for intended and actual error productions. The annotation schema incorporates multiple classificatory dimensions that are of so…

Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems
Zhongsheng Wang, Sijie Wang, Jia Wang, Yung-I Liang, Yuxi Zhang, Jiamou Liu
https://arxiv.org/abs/2507.16843

Weak Supervision Techniques towards Enhanced ASR Models in Industry-level CRM Systems
In the design of customer relationship management (CRM) systems, accurately identifying customer types and offering personalized services are key to enhancing customer satisfaction and loyalty. However, this process faces the challenge of discerning customer voices and intentions, and general pre-trained automatic speech recognition (ASR) models make it difficult to effectively address industry-specific speech recognition tasks. To address this issue, we innovatively proposed a solution for fin…

Towards Robust Speech Recognition for Jamaican Patois Music Transcription
Jordan Madden, Matthew Stone, Dimitri Johnson, Daniel Geddez
https://arxiv.org/abs/2507.16834 https://

Towards Robust Speech Recognition for Jamaican Patois Music Transcription
Although Jamaican Patois is a widely spoken language, current speech recognition systems perform poorly on Patois music, producing inaccurate captions that limit accessibility and hinder downstream applications. In this work, we take a data-centric approach to this problem by curating more than 40 hours of manually transcribed Patois music. We use this dataset to fine-tune state-of-the-art automatic speech recognition (ASR) models, and use the results to develop scaling laws for the performance…

Open Automatic Speech Recognition Models for Classical and Modern Standard Arabic
Lilit Grigoryan, Nikolay Karpov, Enas Albasiri, Vitaly Lavrukhin, Boris Ginsburg
https://arxiv.org/abs/2507.13977

Open Automatic Speech Recognition Models for Classical and Modern Standard Arabic
Despite Arabic being one of the most widely spoken languages, the development of Arabic Automatic Speech Recognition (ASR) systems faces significant challenges due to the language's complexity, and only a limited number of public Arabic ASR models exist. While much of the focus has been on Modern Standard Arabic (MSA), there is considerably less attention given to the variations within the language. This paper introduces a universal methodology for Arabic speech and text processing designed to …

Optimizing ASR for Catalan-Spanish Code-Switching: A Comparative Analysis of Methodologies
Carlos Mena, Pol Serra, Jacobo Romero, Abir Messaoudi, Jose Giraldo, Carme Armentano-Oller, Rodolfo Zevallos, Ivan Meza, Javier Hernando
https://arxiv.org/abs/2507.13875

Optimizing ASR for Catalan-Spanish Code-Switching: A Comparative Analysis of Methodologies
Code-switching (CS), the alternating use of two or more languages, challenges automatic speech recognition (ASR) due to scarce training data and linguistic similarities. The lack of dedicated CS datasets limits ASR performance, as most models rely on monolingual or mixed-language corpora that fail to reflect real-world CS patterns. This issue is critical in multilingual societies where CS occurs in informal and formal settings. A key example is Catalan-Spanish CS, widely used in media and parli…