Audio Language Models Overview

• Audio Language Models are neural architectures that align audio signals with text, using language as a supervision signal for rich event description and reasoning.
• They employ dual encoder and generative models with contrastive and masked modeling strategies to create robust audio–text representations.
• Applications range from audio captioning and question answering to speech separation, emphasizing zero-shot generalization, multilingual support, and safety.

Audio LLMs (ALMs) are neural architectures that process, comprehend, and generate relationships between audio signals and natural language. Unlike conventional supervised audio classification systems relying on predefined categorical labels, ALMs employ language as the supervision signal, enabling complex audio event description, reasoning, and interactive dialogue through their joint audio–text representation space (Su et al., 25 Jan 2025). ALMs have demonstrated strong zero-shot generalization and are foundational to current research in multimodal and audio-centric machine intelligence, underpinning advances in audio captioning, audio question answering (AQA), speech recognition, and cross-modal retrieval.

1. Formal Problem Formulation and Model Architectures

ALMs are defined by dual or unified encoder–decoder architectures that process audio ($a_i$) and paired textual descriptions ($t_i$). A standard dual-tower ALM comprises:

• An audio encoder $f_a(\cdot)$, mapping audio to an embedding $z^a \in \mathbb{R}^d$
• A text encoder $f_t(\cdot)$, mapping text to an embedding $z^t \in \mathbb{R}^d$
• A design objective to align $z^a$ and $z^t$ for semantically related (audio, text) pairs

The architectural taxonomy includes:

• Two-tower (dual encoder): separate audio and text encoders, e.g., CLAP, LAION-CLAP; contrastive training aligns embeddings (Su et al., 25 Jan 2025).
• Two-head generative ALMs: dual encoders feeding an LLM or decoder module; supports audio captioning and text generation, e.g., SpeechGPT, Audio Flamingo 2 (Ghosh et al., 6 Mar 2025).
• Unified single-stream encoder: rare in audio, but analogous to vision-LLMs with joint multimodal attention.
• Agents/cooperation systems: LLM acts as a “planner” orchestrating audio models for complex tasks (Su et al., 25 Jan 2025).

Pretraining strategies span contrastive (symmetric InfoNCE), generative (masked modeling, autoregressive decoding), and discriminative (text–audio matching) paradigms (Su et al., 25 Jan 2025, Selvakumar et al., 21 Oct 2024).

2. Datasets, Data Integrity, and Benchmarking

ALMs derive robustness and generalization from large, high-quality, diverse audio–text datasets. Sources include community platforms (Freesound, YouTube), curated caption corpora (AudioCaps, Clotho), synthetic captioning (WavCaps, DAQA), and specialized benchmarks (MusicCaps, AnimalSpeak) (Wijngaard et al., 9 Jul 2024). Annotation types span tags, full-sentence captions, and Q-A pairs. Principal component analysis confirms that audio embeddings show higher intrinsic dimensionality than textual, and significant modality/category imbalances exist (dominance of “human sounds,” “music”). Data leakage analysis reveals up to 35 million overlapping samples across datasets; use of deduplicated splits is essential for fair benchmarking (Wijngaard et al., 9 Jul 2024).

Zero-shot and downstream performance are assessed via retrieval (recall@K), captioning (BLEU, METEOR, SPICE, CIDEr), classification (accuracy, mAP), and specialized cross-task suites (MMAU, AIR-Bench, AHELM). Recent benchmarks such as AHELM systematize evaluation across ten dimensions: perception, reasoning, bias, fairness, multilinguality, robustness, toxicity, and safety (Lee et al., 29 Aug 2025).

3. Training Objectives, Prompt Learning, and Domain Adaptation

ALMs are typically trained via a symmetric InfoNCE contrastive loss over batches of paired examples:

$$L_{\mathrm{con}} = -\frac{1}{2N} \sum_{i=1}^N \left[ \log\frac{\exp(z_i^a \cdot z_i^t/\tau)}{\sum_{j=1}^N \exp(z_i^a \cdot z_j^t/\tau)} + \log\frac{\exp(z_i^t \cdot z_i^a/\tau)}{\sum_{j=1}^N \exp(z_i^t \cdot z_j^a/\tau)} \right]$$

Prompt learning is crucial for zero-shot and few-shot generalization. Feature-space prompt learning (e.g., PALM (Hanif et al., 29 Sep 2024)) demonstrates state-of-the-art accuracy and computational efficiency over input-space methods by adjusting context vectors in the text encoder output space. Test-time unsupervised domain adaptation is achieved by learning a domain token/vector, minimizing prediction entropy across augmented audio views (Deshmukh et al., 14 Feb 2024, Chen et al., 23 Dec 2024); such approaches yield up to 8.4% improvement and preserve generalization in cross-domain evaluations.

Compositional reasoning and temporal order remain challenging. Modular contrastive losses and composition-aware hard negative mining (CompA-CLAP (Ghosh et al., 2023)) significantly improve ALMs’ ability to recognize event order and attribute bindings. Self-supervised temporal instillation (TeminAL (Sinha et al., 17 Aug 2024)) further enhances time awareness without degrading retrieval strengths, using stagewise training over concatenated and time-reversed audio–text pairs.

4. Multilingual, Robustness, and Safety Properties

Recent ALMs (SeaLLMs-Audio (Liu et al., 3 Nov 2025)) explicitly support multilingual understanding (Indonesian, Thai, Vietnamese, English, Chinese), multimodal input handling (audio/text/mixed), and simultaneous multi-tasking (captioning, ASR, translation, emotion recognition, QA, summarization). Balanced, language-diverse corpora and inclusive benchmarks (SeaBench-Audio) demonstrate robust generalization and reduced code-switching.

Robustness to adversarial audio is a growing concern. Universal audio jailbreaks can bypass alignment layers and embed imperceptible first-person toxic cues in audio signals (Gupta et al., 2 Feb 2025). Transferability and stealth are empirically proven, and attack resistance persists under real-world degradations (speaker playback, noise). Countermeasures employing adversarial training, domain-aware filtering, and anomaly detection on audio embeddings are proposed.

Detection of ALM-based deepfakes achieves 0% EER under most conditions for codec-trained countermeasures, but challenges remain for out-of-domain or music audio, pointing to the need for broader corpus coverage and type-specific feature specialization (Xie et al., 20 Aug 2024).

5. Applications: Reasoning, Captioning, Quality, and Separation

ALMs are utilized for:

• Audio captioning: free-form description of scenes, objects, or actions in sound, with state-of-the-art models (Audio Flamingo 2 (Ghosh et al., 6 Mar 2025)) leveraging synthetic fine-grained QA data, curriculum learning, and cross-attention fusion.
• Audio question answering (AQA): both closed and open-form reasoning across out-of-distribution domains. Small ALMs (Mellow (Deshmukh et al., 11 Mar 2025)) trained on synthetic reasoning data rival SoTA large models (Qwen2-Audio) on MMAU, ACE, CLD, ACD, and binary QA, achieving high accuracy with compute efficiency.
• Deductive reasoning: audio entailment is addressed via multi-way classification on curated audio–hypothesis pairs (ACE, CLE), with explicit intermediate captioning reducing hallucination and boosting accuracy by up to 6% (Deshmukh et al., 25 Jul 2024).
• Audio quality assessment: PAM (Deshmukh et al., 1 Feb 2024) transforms ALMs into zero-shot audio quality metrics using opposed prompts (“clean” vs. “noisy/artifact”), outperforming traditional baselines in cross-domain evaluations without reference signals.
• Speech separation: SepALM (Mu et al., 6 May 2025) integrates ALMs as end-to-end error correctors and resynthesizers, using chain-of-thought prompting and distillation to mitigate error accumulation and adapt to novel acoustic environments.

6. Generative and Continuous-Token Paradigms

Generative ALMs for speech and music treat discrete neural-codec tokens (SoundStream, EnCodec) as sequential “language,” enabling text-to-speech (TTS), text-to-music (TTM), and universal audio synthesis (Xie et al., 20 Aug 2024). Recent continuous audio LLMs (CALM (Simon et al., 8 Sep 2025)) forgo lossy quantization, using Transformer backbones and consistency-model heads to autoregressively generate audio VAE latents, achieving higher fidelity and lower generation costs, and outperforming token-based baselines on various perceptual metrics.

7. Open Challenges and Future Directions

Persistent gaps include:

• Compositional reasoning: extending order and attribute binding to richer taxonomies and real scene mixtures (Ghosh et al., 2023).
• Scaling laws: empirical characterization of data/parameter trade-offs, especially for small ALMs targeting edge deployment (Deshmukh et al., 11 Mar 2025).
• Multilingual/cross-domain adaptation: corpus expansion, language balancing, and eliminating code-switching remain ongoing efforts (Liu et al., 3 Nov 2025).
• Robustness/safety: defenses against universal jailbreaks, adversarial examples, and deepfake detection need continual improvement, including cross-modal anomaly gating (Gupta et al., 2 Feb 2025, Xie et al., 20 Aug 2024).
• Temporal compositionality: ALMs have only begun to acquire temporal order understanding; future architectures must natively model event chains and fine-grained temporal relations (Sinha et al., 17 Aug 2024).
• Benchmarking: AHELM (Lee et al., 29 Aug 2025) and SeaBench-Audio exemplify holistic, transparent evaluation protocols; continual evolution with new dimensions and open-weight models is recommended.

Audio LLMs form the computational foundation for audio-centric machine perception, multimodal reasoning, dialogue, and generation, integrating and extending advances in self-supervised representation, large-scale LLMs, and cross-modal fusion. Continued progress will depend on advances in data integrity, pretraining strategies, compositional reasoning, robustness, and multilingual adaptation.