# Resurfacing Paralinguistic Awareness in Large Audio Language Models

arXiv: https://arxiv.org/abs/2603.11947 · HTML: https://mlsanity.com/paper/2603.11947/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 29 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 26 of 26)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context](https://mlsanity.com/paper/2403.05530/) (2024)
- [Qwen2.5-Omni Technical Report](https://mlsanity.com/paper/2503.20215/) (2025)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [Qwen2-Audio Technical Report](https://mlsanity.com/paper/2407.10759/) (2024)
- [Kimi-Audio Technical Report](https://mlsanity.com/paper/2504.18425/) (2025)
- [VoiceBench: Benchmarking LLM-Based Voice Assistants](https://mlsanity.com/paper/2410.17196/) (2024)
- [Speech Model Pre-training for End-to-End Spoken Language Understanding](https://mlsanity.com/paper/1904.03670/) (2019)
- [Advancing Large Language Models to Capture Varied Speaking Styles and Respond Properly in Spoken Conversations](https://mlsanity.com/paper/2402.12786/) (2024)
- [Towards Holistic Evaluation of Large Audio-Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2505.15957/) (2025)
- [Audio Is the Achilles' Heel: Red Teaming Audio Large Multimodal Models](https://mlsanity.com/paper/2410.23861/) (2024)
- [BLSP-Emo: Towards Empathetic Large Speech-Language Models](https://mlsanity.com/paper/2406.03872/) (2024)
- [Paralinguistics-Enhanced Large Language Modeling of Spoken Dialogue](https://mlsanity.com/paper/2312.15316/) (2023)
- [ParaS2S: Benchmarking and Aligning Spoken Language Models for Paralinguistic-aware Speech-to-Speech Interaction](https://mlsanity.com/paper/2511.08723/) (2025)
- [OSUM-EChat: Enhancing End-to-End Empathetic Spoken Chatbot via Understanding-Driven Spoken Dialogue](https://mlsanity.com/paper/2508.09600/) (2025)
- [E-chat: Emotion-sensitive Spoken Dialogue System with Large Language Models](https://mlsanity.com/paper/2401.00475/) (2023)
- [SpeechGuard: Exploring the Adversarial Robustness of Multimodal Large Language Models](https://mlsanity.com/paper/2405.08317/) (2024)
- [Investigating Pre-trained Audio Encoders in the Low-Resource Condition](https://mlsanity.com/paper/2305.17733/) (2023)
- [SPIRIT: Patching Speech Language Models against Jailbreak Attacks](https://mlsanity.com/paper/2505.13541/) (2025)
- [Reshaping Representation Space to Balance the Safety and Over-rejection in Large Audio Language Models](https://mlsanity.com/paper/2505.19670/) (2025)
- [Towards Probing Speech-Specific Risks in Large Multimodal Models: A Taxonomy, Benchmark, and Insights](https://mlsanity.com/paper/2406.17430/) (2024)
- [Layer-wise Minimal Pair Probing Reveals Contextual Grammatical-Conceptual Hierarchy in Speech Representations](https://mlsanity.com/paper/2509.15655/) (2025)
- [GOAT-SLM: A Spoken Language Model with Paralinguistic and Speaker Characteristic Awareness](https://mlsanity.com/paper/2507.18119/) (2025)
- [Reflecting Twice before Speaking with Empathy: Self-Reflective Alternating Inference for Empathy-Aware End-to-End Spoken Dialogue](https://mlsanity.com/paper/2601.18281/) (2026)

## Built on by (most cited first, 2 of 2)

- [VoxSafeBench: Not Just What Is Said, but Who, How, and Where](https://mlsanity.com/paper/2604.14548/) (2026)
- [Encoder-Side Neuron Identification and Amplification for Acoustic Perception in Large Audio-Language Models](https://mlsanity.com/paper/2607.11801/) (2026)

## Abstract (arXiv)

Large Audio Language Models (LALMs) have expanded the interaction with human to speech modality, which introduces great interactive potential, due to the paralinguistic cues implicitly indicating the user context. However, building on the current content-centred paradigm, LALMs usually neglect such paralinguistic cues and respond solely based on query content. In this work, to resurface the paralinguistic awareness in LALMs, we introduce five diverse layer-wise analyses to jointly identify paralinguistic layers and semantic understanding layers. Based on these insights, we propose a paralinguistic-enhanced fine-tuning (PE-FT) protocol accordingly to equip LALMs with paralinguistic-aware capabilities, including (1) selective-layer fine-tuning, and (2) an auxiliary dual-level classification head. Our experiments demonstrate that PE-FT protocol efficiently and effectively resurfaces the paralinguistic awareness, even surpassing the performance of the all-layer fine-tuning strategy.