arXiv AI
CAL-MOS: Bridging Layers with Adapters for Robust MOS Prediction Across Speech Foundation Models
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arXiv AI
Sep 18
Scaling Audio Models Efficiently: Joint Optimization of Scale, Resolution, Adaptation, Precision, and Sparsity
The paper introduces a compression framework for the Whisper automatic speech recognition model that jointly optimizes six deployment dimensions—model size, temporal resolution, encoder token stride, low‑rank adaptation capacity, weight precision, and sparsity pattern—using NSGA‑III. The optimization targets three objectives: word error rate, inference FLOPs, and memory footprint. Evaluating 1,680 configurations, the study identifies compression combinations that outperform single‑axis scaling and notes that 1:4 structured sparsity cannot maintain acceptable accuracy within the tested budgets.
arXiv Computation and Language
Sep 17
Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs
arXiv:2609.17981v1 Announce Type: cross Abstract: Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-R...
arXiv AI
Jun 29
Measuring the Redundancy of Decoder Layers in SpeechLLMs
arXiv:2603. 05121v2 Announce Type: replace-cross Abstract: Speech Large Language Models route speech encoder representations into an LLM decoder that typically accounts for over 90% of total parameters.
Hugging Face Trending Papers
Sep 16
Encoder Awakening via Adapters: Effective Domain-Adaptive Fine-tuning of Speech-LLMs
Speech Large Language Models (Speech-LLMs), typically built from a pre-trained speech encoder, a modality projector, and an LLM fine-tuned with Low-Rank Adapters (LoRA), have shown strong Automatic Sp...
arXiv AI
Jun 11
Fast Speech Foundation Model Distillation Using Interleaved Stacking
arXiv:2606. 11766v1 Announce Type: cross Abstract: Distilling a large speech foundation model (SFM) into an efficient student model has been successfully applied to low-resource environments.
Hugging Face Trending Papers
Oct 19
Manifold-Constrained Residual Adaptation with Frame-Adaptive Layer Aggregation for Speaker Verification
Large self-supervised speech models provide powerful representations for speaker verification, but full fine-tuning is expensive. Most conventional adapters insert small trainable modules to modify th...