This paper proposes a novel pretraining method for Acoustic-to-Articulatory Inversion (AAI) using Phoneme Labels, Articulatory Feature Labels, and Critical-articulator Labels, improving performance an…

The study investigates the limitations of Phonetic Foundation Models (PFMs) for Speech-to-IPA transcription using Grapheme-to-Phoneme (G2P) labels and proposes a new approach based on continuous Artic…

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…

A lightweight framework for automated pronunciation assessment using native speech resources and unsupervised or lightly calibrated methods.

The paper introduces BioConCal, a supervised scoring mechanism that evaluates biomedical NER candidates surfaced by multiple LLMs, significantly improving the quality of the candidate pool for human c…

This paper systematically evaluates acoustic-to-articulatory inversion under domain shifts on FROST-EMA, a Finnish-Russian bilingual EMA corpus, and establishes benchmarks for articulatory targets, ac…

This paper investigates the use of various acoustic features for recognizing dysarthric speech using a Factorized Time Delay Neural Network (F-TDNN) model, achieving a relative improvement of 4.65% in…

This paper introduces the Kinematic-Aware Articulation Interface (KAI), a representation that captures the kinematic structure of articulated objects, improving sample efficiency and generalization in…

The paper proposes RTFree-F5, a method to make flow-matching TTS models like F5-TTS independent of reference transcripts, improving performance and naturalness for dysarthric speakers.

The paper introduces GigaSpeechBench, a comprehensive multilingual and multidimensional ASR & AST benchmark with 680 hours of human-annotated speech, featuring 12 low-resource languages, 6 Chinese dia…

This paper proposes an SSL-AutoEncoder (SSL-AE) approach for reducing feature dimensions in self-supervised learning models while maintaining dysarthric ASR performance.

The paper proposes Pitch-Accent-focused Speech Quality Assessment (PASQA) to explicitly target pitch-accent correctness in speech quality assessment, using a controlled Japanese accent-error dataset a…

This paper introduces WavLM for vocal effort classification and improves performance through data augmentation and Gaussian-neighbor soft labels, achieving a new state-of-the-art on AVID.

The paper introduces Script-Normalized WER (SN-WER), a novel evaluation metric that transliterates ASR transcripts into a canonical script to accurately measure speech recognition performance across d…

This paper introduces the concept of Safety Geometry Collapse, demonstrating that multimodal inputs degrade the safety separation of LLMs, and proposes ReGap, a training-free method that adaptively co…

TalTech submitted top-ranking systems to the Beyond Transcription Challenge using fine-tuned Voxtral models and reinforcement learning against Open Medical Concept F1.