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eess.AS2026

Few-Shot and Pseudo-Label Guided Speech Quality Evaluation with Large Language Models

Ryandhimas E. Zezario, Dyah A. M. G. Wisnu, Szu-Wei Fu +3

In this paper, we introduce GatherMOS, a novel framework that leverages large language models (LLM) as meta-evaluators to aggregate diverse signals into quality predictions. Gather…

eess.AS2026

How Auditory Knowledge in LLM Backbones Shapes Audio Language Models: A Holistic Evaluation

Ke-Han Lu, Szu-Wei Fu, Chao-Han Huck Yang +13

Large language models (LLMs) have been widely used as knowledge backbones of Large Audio Language Models (LALMs), yet how much auditory knowledge they encode through text-only pre-…

eess.AS2026

DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment

Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +25

We introduce DeSTA2.5-Audio, a general-purpose Large Audio Language Model (LALM) designed for robust auditory perception and instruction-following. Recent LALMs augment Large Langu…

eess.AS2025

HighRateMOS: Sampling-Rate Aware Modeling for Speech Quality Assessment

Wenze Ren, Yi-Cheng Lin, Wen-Chin Huang +9

Modern speech quality prediction models are trained on audio data resampled to a specific sampling rate. When faced with higher-rate audio at test time, these models can produce bi…

eess.AS2025

DeSTA2: Developing Instruction-Following Speech Language Model Without Speech Instruction-Tuning Data

Ke-Han Lu, Zhehuai Chen, Szu-Wei Fu +5

Recent end-to-end speech language models (SLMs) have expanded upon the capabilities of large language models (LLMs) by incorporating pre-trained speech models. However, these SLMs…

eess.AS2024

Generative Speech Foundation Model Pretraining for High-Quality Speech Extraction and Restoration

Pin-Jui Ku, Alexander H. Liu, Roman Korostik +3

This paper proposes a generative pretraining foundation model for high-quality speech restoration tasks. By directly operating on complex-valued short-time Fourier transform coeffi…