Tootfinder

Towards Accurate Phonetic Error Detection Through Phoneme Similarity Modeling
Xuanru Zhou, Jiachen Lian, Cheol Jun Cho, Tejas Prabhune, Shuhe Li, William Li, Rodrigo Ortiz, Zoe Ezzes, Jet Vonk, Brittany Morin, Rian Bogley, Lisa Wauters, Zachary Miller, Maria Gorno-Tempini, Gopala Anumanchipalli
https://arxiv.org/abs/2507.14346…

Towards Accurate Phonetic Error Detection Through Phoneme Similarity Modeling
Phonetic error detection, a core subtask of automatic pronunciation assessment, identifies pronunciation deviations at the phoneme level. Speech variability from accents and dysfluencies challenges accurate phoneme recognition, with current models failing to capture these discrepancies effectively. We propose a verbatim phoneme recognition framework using multi-task training with novel phoneme similarity modeling that transcribes what speakers actually say rather than what they're supposed to s…

A look at Tea, a woman-only safety app with 4M users that lets users anonymously assign red or green flags to local men, as it goes viral with 900K new signups (Angela Yang/NBC News)
https://www.nbcnews.com/tech/tech-news/women-are-an…

Women are anonymously spilling tea about men in their cities on viral app
When Cid Walker opens the Tea app, she’s greeted by a barrage of posts about men and their apparent "red" and "green" "flags."“He’s a cheater,” Walker said, reading some of the comments on one post out loud."What clubs does he go to?" another person asked on a different post.

"Despite good-faith efforts by DSI [dense street imagery] providers to protect individual privacy through blurring faces and license plates, these measures fail to address broader privacy concerns. In this work, we find that increased data density and advancements in artificial intelligence enable harmful group membership inferences from supposedly anonymized data."
via @…

Privacy of Groups in Dense Street Imagery
Spatially and temporally dense street imagery (DSI) datasets have grown unbounded. In 2024, individual companies possessed around 3 trillion unique images of public streets. DSI data streams are only set to grow as companies like Lyft and Waymo use DSI to train autonomous vehicle algorithms and analyze collisions. Academic researchers leverage DSI to explore novel approaches to urban analysis. Despite good-faith efforts by DSI providers to protect individual privacy through blurring faces and l…

2022 waren wir mit der Schachtour in Schweinfurt im Wildpark an den Eichen und haben dort geholfen, ein Blitzturnier zu veranstalten. Neben Flyern, Luftballons etc. war auch hier natürlich zur Freude aller Kinder wieder Chessy am Start. Und auch unsere "Morphs" waren mit dabei, sodass man gegen anonyme Gegner Schach spielen konnte.
Falls ihr aus dem Osten kommt und Lust habt, dass wir diesen Sommer zu euch kommen, meldet euch gerne hier an:

Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis
Zongli Ye, Jiachen Lian, Xuanru Zhou, Jinming Zhang, Haodong Li, Shuhe Li, Chenxu Guo, Anaisha Das, Peter Park, Zoe Ezzes, Jet Vonk, Brittany Morin, Rian Bogley, Lisa Wauters, Zachary Miller, Maria Gorno-Tempini, Gopala Anumanchipalli
https://arxiv.org/abs/2…

Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis
Accurate alignment of dysfluent speech with intended text is crucial for automating the diagnosis of neurodegenerative speech disorders. Traditional methods often fail to model phoneme similarities effectively, limiting their performance. In this work, we propose Neural LCS, a novel approach for dysfluent text-text and speech-text alignment. Neural LCS addresses key challenges, including partial alignment and context-aware similarity mapping, by leveraging robust phoneme-level modeling. We eval…

LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness
Zongli Ye, Jiachen Lian, Akshaj Gupta, Xuanru Zhou, Krish Patel, Haodong Li, Hwi Joo Park, Chenxu Guo, Shuhe Li, Sam Wang, Cheol Jun Cho, Zoe Ezzes, Jet M. J. Vonk, Brittany T. Morin, Rian Bogley, Lisa Wauters, Zachary A. Miller, Maria Luisa Gorno-Tempini, Gopala Anumanchipalli
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LCS-CTC: Leveraging Soft Alignments to Enhance Phonetic Transcription Robustness
Phonetic speech transcription is crucial for fine-grained linguistic analysis and downstream speech applications. While Connectionist Temporal Classification (CTC) is a widely used approach for such tasks due to its efficiency, it often falls short in recognition performance, especially under unclear and nonfluent speech. In this work, we propose LCS-CTC, a two-stage framework for phoneme-level speech recognition that combines a similarity-aware local alignment algorithm with a constrained CTC …