Hugging Face Trending Papers June 17, 2026

PrefSQA: Pairwise Preference Prediction for Speech Quality Assessment and the Critical Role of High Quality Datasets

Read the original on Hugging Face Trending Papers →

Mean opinion scores (MOS) are widely used for speech quality assessment, yet scalar labels are sensitive to rater variability and listening test differences. This introduces labeling noise, which limits the reliability of MOS prediction.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at Hugging Face Trending Papers.

arXiv AI

Jun 19

PrefSQA: Pairwise Preference Prediction for Speech Quality Assessment and the Critical Role of High Quality Datasets

arXiv:2606. 19597v1 Announce Type: cross Abstract: Mean opinion scores (MOS) are widely used for speech quality assessment, yet scalar labels are sensitive to rater variability and listening test differences.

By Junyi Fan, Donald S. Williamson

arXiv Machine Learning

Jun 19

Investigating Human-Model Discrepancies in Speech Quality Assessment via Acoustic and Prosodic Perturbations

arXiv:2606. 19951v1 Announce Type: cross Abstract: Mean opinion score (MOS) prediction models are widely used as proxy metrics in text-to-speech (TTS) research, yet their ability to capture quality differences beyond acoustic fidelity remains unclear.

By Masato Takagi, Masaya Kawamura, Reo Shimizu, Yuma Shirahata

robotics multimodal

arXiv AI

Sep 15

Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment

arXiv:2605.00022v2 Announce Type: replace-cross Abstract: The rapid proliferation of large audio models (LAMs) demands efficient approaches for model comparison, yet comprehensive benchmarks are cost...

By Woody Haosheng Gan, William Held, Diyi Yang

benchmarks safety

arXiv Machine Learning

Sep 15

The Limits of Reference-Free Speech Quality Metrics as Evaluators and Rewards on Modern Text-to-Speech

arXiv:2609.13150v1 Announce Type: cross Abstract: Reference-free quality predictors such as UTMOS, DNSMOS and SCOREQ are the de facto automatic evaluators for text-to-speech (TTS) and are increasingl...

By Antonis Asonitis, Juan Pablo Zuluaga Gomez, Francesco Verdini, Aref Farhadipour, Marzieh Razavi, Pierre-Edouard Honnet, Vijeta Avijeet

multimodal

arXiv Machine Learning

Jun 19

PASQA: Pitch-Accent-Focused Speech Quality Assessment Model Trained on Synthetic Speech with Accent Errors

arXiv:2606. 20137v1 Announce Type: cross Abstract: Existing mean opinion score (MOS) prediction models typically predict utterance-level naturalness MOS and can be insensitive to localized pitch-accent errors.

By Masaya Kawamura, Yuma Shirahata, Kentaro Mitsui, Reo Shimizu

multimodal

arXiv AI

Aug 24

DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization

arXiv:2608.21176v1 Announce Type: cross Abstract: Automatic speech quality assessment aims to predict Mean Opinion Scores (MOS) consistent with human subjective perception and is essential for evalua...

By Naiyuan Li, Li Dong, Diqun Yan

benchmarks

One email a morning, machine-written

Related stories

PrefSQA: Pairwise Preference Prediction for Speech Quality Assessment and the Critical Role of High Quality Datasets

Investigating Human-Model Discrepancies in Speech Quality Assessment via Acoustic and Prosodic Perturbations

Putting HUMANS first: Efficient LAM Evaluation with Human Preference Alignment

The Limits of Reference-Free Speech Quality Metrics as Evaluators and Rewards on Modern Text-to-Speech

PASQA: Pitch-Accent-Focused Speech Quality Assessment Model Trained on Synthetic Speech with Accent Errors

DAMOS: Learning Distortion-Aware Speech Quality Assessment through Explicit Distortion Localization