Caption Studio is a transparency-first speech and audio intelligence platform that provides automated transcription, speaker diarization, speech analytics, signal-level audio analysis, and subtitle ge…

The paper introduces Target Speaker Tagging (TST), a task that combines speaker diarization, verification, and identification into a single workflow for multi-speaker conversations. It presents TST-Be…

Echo is a joint-embedding predictive architecture that uses a single, pretrained ViT encoder to simultaneously perform speaker diarization, speech recognition, and dynamic source separation in a share…

The paper proposes a novel workflow to extract fine-grained regional accent features in Brazilian Portuguese using only acoustic labels and a phoneme-based forced aligner, showing that localized featu…

The paper introduces a supervised fine-tuning pipeline using large language models to accurately categorize sentence-level clinical provenance across multi-disciplinary hospital notes, demonstrating t…

The paper quantitatively confirms the Currier A/B language distinction in the Voynich Manuscript, demonstrating it is governed by a higher-dimensional, context-dependent boolean switch rather than a s…

This paper measures and analyzes the overuse of epanorthosis, a rhetorical figure, in large language models and proposes techniques to mitigate it.

This paper proposes using homoglyphic substitution, replacing characters with visually similar alternatives, as a method to degrade and prevent the extraction of personal information via adversarial s…

This paper proposes a method for language identification using compositional vectors and the centered log-ratio transformation, achieving robust accuracy and strong performance for longer sequences.

The paper introduces LLMSurgeon, a framework that estimates the domain-level data mixture of a Large Language Model (LLM) using only generated text, thereby providing a post-hoc method to audit the mo…

This paper introduces 'implicit identity' as a unifying framework to survey and categorize LLM fingerprinting and watermarking techniques for verifying ownership and provenance across datasets, models…

The paper formalizes the problem of representation identifiability in supervised learning, showing that a representation property is identifiable if and only if it is constant across all possible fact…

The study tests the validity of Portugal's AMALIA language model by examining its ability to follow a construct's theory and not just rely on surface correlates.

This paper presents a method for identifying probabilistic structures from empirical probability tensors using algebraic statistics and Kronecker-stack class of configuration matrices.

A regression-based approach is presented for Arabic dialect geolocation using a hierarchical neural architecture and spherical geodesic loss, achieving a median localization error of 481.2 km.

The paper applies network science to demonstrate the feasibility of unsupervised dependency parsing in non-human primates due to their sequence length distribution.

This paper uses continual learning with explicit disfluency tokens to improve Automatic Speech Recognition (ASR) systems on disfluent speech, addressing the information loss and hallucinations caused…

The paper enhances French parsing accuracy by integrating data from a syntactic lexicon and applying word clustering methods to verbs within a Probabilistic Context-Free Grammar framework.