Tootfinder

Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Anyu Ying, Natarajan Balaji Shankar, Chyi-Jiunn Lin, Mohan Shi, Pu Wang, Hye-jin Shim, Siddhant Arora, Hugo Van hamme, Abeer Alwan, Shinji Watanabe
https://arxiv.org/abs/2508.16576

Benchmarking Training Paradigms, Dataset Composition, and Model Scaling for Child ASR in ESPnet
Despite advancements in ASR, child speech recognition remains challenging due to acoustic variability and limited annotated data. While fine-tuning adult ASR models on child speech is common, comparisons with flat-start training remain underexplored. We compare flat-start training across multiple datasets, SSL representations (WavLM, XEUS), and decoder architectures. Our results show that SSL representations are biased toward adult speech, with flat-start training on child speech mitigating the…

Evaluating the Safety and Skill Reasoning of Large Reasoning Models Under Compute Constraints
Adarsha Balaji, Le Chen, Rajeev Thakur, Franck Cappello, Sandeep Madireddy
https://arxiv.org/abs/2509.18382

Mah\={a}n\={a}ma: A Unique Testbed for Literary Entity Discovery and Linking
Sujoy Sarkar, Gourav Sarkar, Manoj Balaji Jagadeeshan, Jivnesh Sandhan, Amrith Krishna, Pawan Goyal
https://arxiv.org/abs/2509.19844

Mahānāma: A Unique Testbed for Literary Entity Discovery and Linking
High lexical variation, ambiguous references, and long-range dependencies make entity resolution in literary texts particularly challenging. We present Mahānāma, the first large-scale dataset for end-to-end Entity Discovery and Linking (EDL) in Sanskrit, a morphologically rich and under-resourced language. Derived from the Mahābhārata, the world's longest epic, the dataset comprises over 109K named entity mentions mapped to 5.5K unique entities, and is aligned with an English knowledge base…

Balaji revealed his shocking ideas for a tech-governed city
where citizens loyal to tech companies would form a new political tribe clad in
gray t-shirts.
“And if you see another Gray on the street … you do the nod,” he said, during a four-hour talk on the Moment of Zen podcast. “You’re a fellow Gray.”
“Grays should embrace the police, okay? All-in on the police,”
said Balaji Srinivasan.
“What does that mean? That’s, as I said, banquets.
That means …

The Tech Baron Seeking to Purge San Francisco of “Blues”
If Balaji Srinivasan is any guide, then the Silicon Valley plutocrats are definitely not OK.

Investigating Polyglot Speech Foundation Models for Learning Collective Emotion from Crowds
Orchid Chetia Phukan, Girish, Mohd Mujtaba Akhtar, Panchal Nayak, Priyabrata Mallick, Swarup Ranjan Behera, Parabattina Bhagath, Pailla Balakrishna Reddy, Arun Balaji Buduru
https://arxiv.org/abs/2509.16329…

Investigating Polyglot Speech Foundation Models for Learning Collective Emotion from Crowds
This paper investigates the polyglot (multilingual) speech foundation models (SFMs) for Crowd Emotion Recognition (CER). We hypothesize that polyglot SFMs, pre-trained on diverse languages, accents, and speech patterns, are particularly adept at navigating the noisy and complex acoustic environments characteristic of crowd settings, thereby offering a significant advantage for CER. To substantiate this, we perform a comprehensive analysis, comparing polyglot, monolingual, and speaker recognitio…

Crypto and tech entrepreneurs are attending Network School, founded by Balaji Srinivasan in Forest City, Malaysia, to test his concepts about the Network State (Ryan Weeks/Bloomberg)
https://www.bloomberg.com/news/articles/20

Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency
Kaiwen Zheng, Yuji Wang, Qianli Ma, Huayu Chen, Jintao Zhang, Yogesh Balaji, Jianfei Chen, Ming-Yu Liu, Jun Zhu, Qinsheng Zhang
https://arxiv.org/abs/2510.08431

Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency
This work represents the first effort to scale up continuous-time consistency distillation to general application-level image and video diffusion models. Although continuous-time consistency model (sCM) is theoretically principled and empirically powerful for accelerating academic-scale diffusion, its applicability to large-scale text-to-image and video tasks remains unclear due to infrastructure challenges in Jacobian-vector product (JVP) computation and the limitations of standard evaluation …

Are Multimodal Foundation Models All That Is Needed for Emofake Detection?
Mohd Mujtaba Akhtar, Girish, Orchid Chetia Phukan, Swarup Ranjan Behera, Pailla Balakrishna Reddy, Ananda Chandra Nayak, Sanjib Kumar Nayak, Arun Balaji Buduru
https://arxiv.org/abs/2509.16193

Are Multimodal Foundation Models All That Is Needed for Emofake Detection?
In this work, we investigate multimodal foundation models (MFMs) for EmoFake detection (EFD) and hypothesize that they will outperform audio foundation models (AFMs). MFMs due to their cross-modal pre-training, learns emotional patterns from multiple modalities, while AFMs rely only on audio. As such, MFMs can better recognize unnatural emotional shifts and inconsistencies in manipulated audio, making them more effective at distinguishing real from fake emotional expressions. To validate our hy…

Rethinking Cross-Corpus Speech Emotion Recognition Benchmarking: Are Paralinguistic Pre-Trained Representations Sufficient?
Orchid Chetia Phukan, Mohd Mujtaba Akhtar, Girish, Swarup Ranjan Behera, Parabattina Bhagath, Pailla Balakrishna Reddy, Arun Balaji Buduru
https://arxiv.org/abs/2509.16182

Rethinking Cross-Corpus Speech Emotion Recognition Benchmarking: Are Paralinguistic Pre-Trained Representations Sufficient?
Recent benchmarks evaluating pre-trained models (PTMs) for cross-corpus speech emotion recognition (SER) have overlooked PTM pre-trained for paralinguistic speech processing (PSP), raising concerns about their reliability, since SER is inherently a paralinguistic task. We hypothesize that PSP-focused PTM will perform better in cross-corpus SER settings. To test this, we analyze state-of-the-art PTMs representations including paralinguistic, monolingual, multilingual, and speaker recognition. Ou…

Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction
Roshan Balaji, Joe Bobby, Nirav Pravinbhai Bhatt
https://arxiv.org/abs/2509.09619 h…

Functional Groups are All you Need for Chemically Interpretable Molecular Property Prediction
Molecular property prediction using deep learning (DL) models has accelerated drug and materials discovery, but the resulting DL models often lack interpretability, hindering their adoption by chemists. This work proposes developing molecule representations using the concept of Functional Groups (FG) in chemistry. We introduce the Functional Group Representation (FGR) framework, a novel approach to encoding molecules based on their fundamental chemical substructures. Our method integrates two t…