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

arXiv: https://arxiv.org/abs/2409.20007 · HTML: https://mlsanity.com/paper/2409.20007/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 37 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## Builds on (most cited first, 30 of 32)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [PaLM 2 Technical Report](https://mlsanity.com/paper/2305.10403/) (2023)
- [VoxCeleb: a large-scale speaker identification dataset](https://mlsanity.com/paper/1706.08612/) (2017)
- [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)
- [SALMONN: Towards Generic Hearing Abilities for Large Language Models](https://mlsanity.com/paper/2310.13289/) (2023)
- [AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head](https://mlsanity.com/paper/2304.12995/) (2023)
- [NeMo: a toolkit for building AI applications using Neural Modules](https://mlsanity.com/paper/1909.09577/) (2019)
- [AIR-Bench: Benchmarking Large Audio-Language Models via Generative Comprehension](https://mlsanity.com/paper/2402.07729/) (2024)
- [Listen, Think, and Understand](https://mlsanity.com/paper/2305.10790/) (2023)
- [emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation](https://mlsanity.com/paper/2312.15185/) (2023)
- [PromptTTS: Controllable Text-to-Speech with Text Descriptions](https://mlsanity.com/paper/2211.12171/) (2022)
- [WavLLM: Towards Robust and Adaptive Speech Large Language Model](https://mlsanity.com/paper/2404.00656/) (2024)
- [Dynamic-SUPERB: Towards A Dynamic, Collaborative, and Comprehensive Instruction-Tuning Benchmark for Speech](https://mlsanity.com/paper/2309.09510/) (2023)
- [Joint Audio and Speech Understanding](https://mlsanity.com/paper/2309.14405/) (2023)
- [BLSP: Bootstrapping Language-Speech Pre-training via Behavior Alignment of Continuation Writing](https://mlsanity.com/paper/2309.00916/) (2023)
- [DailyTalk: Spoken Dialogue Dataset for Conversational Text-to-Speech](https://mlsanity.com/paper/2207.01063/) (2022)
- [SpeechVerse: A Large-scale Generalizable Audio Language Model](https://mlsanity.com/paper/2405.08295/) (2024)
- [AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs](https://mlsanity.com/paper/2311.06753/) (2023)
- [BLSP-Emo: Towards Empathetic Large Speech-Language Models](https://mlsanity.com/paper/2406.03872/) (2024)
- [DeSTA: Enhancing Speech Language Models through Descriptive Speech-Text Alignment](https://mlsanity.com/paper/2406.18871/) (2024)
- [Speech-Copilot: Leveraging Large Language Models for Speech Processing via Task Decomposition, Modularization, and Program Generation](https://mlsanity.com/paper/2407.09886/) (2024)
- [Brouhaha: multi-task training for voice activity detection, speech-to-noise ratio, and C50 room acoustics estimation](https://mlsanity.com/paper/2210.13248/) (2022)

## Built on by (most cited first, 30 of 45)

- [On The Landscape of Spoken Language Models: A Comprehensive Survey](https://mlsanity.com/paper/2504.08528/) (2025)
- [DeSTA2.5-Audio: Toward General-Purpose Large Audio Language Model with Self-Generated Cross-Modal Alignment](https://mlsanity.com/paper/2507.02768/) (2025)
- [SAKURA: On the Multi-hop Reasoning of Large Audio-Language Models Based on Speech and Audio Information](https://mlsanity.com/paper/2505.13237/) (2025)
- [Speech-IFEval: Evaluating Instruction-Following and Quantifying Catastrophic Forgetting in Speech-Aware Language Models](https://mlsanity.com/paper/2505.19037/) (2025)
- [Reducing Object Hallucination in Large Audio-Language Models via Audio-Aware Decoding](https://mlsanity.com/paper/2506.07233/) (2025)
- [Audio MultiChallenge: A Multi-Turn Evaluation of Spoken Dialogue Systems on Natural Human Interaction](https://mlsanity.com/paper/2512.14865/) (2025)
- [AudioLens: A Closer Look at Auditory Attribute Perception of Large Audio-Language Models](https://mlsanity.com/paper/2506.05140/) (2025)
- [TASTE: Text-Aligned Speech Tokenization and Embedding for Spoken Language Modeling](https://mlsanity.com/paper/2504.07053/) (2025)
- [MCIF: Multimodal Crosslingual Instruction-Following Benchmark from Scientific Talks](https://mlsanity.com/paper/2507.19634/) (2025)
- [Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs](https://mlsanity.com/paper/2503.06362/) (2025)
- [Teaching Audio-Aware Large Language Models What Does Not Hear: Mitigating Hallucinations through Synthesized Negative Samples](https://mlsanity.com/paper/2505.14518/) (2025)
- [Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models](https://mlsanity.com/paper/2505.17496/) (2025)
- [OpenOmni: Advancing Open-Source Omnimodal Large Language Models with Progressive Multimodal Alignment and Real-Time Self-Aware Emotional Speech Synthesis](https://mlsanity.com/paper/2501.04561/) (2025)
- [Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach](https://mlsanity.com/paper/2505.14336/) (2025)
- [ESPnet-SpeechLM: An Open Speech Language Model Toolkit](https://mlsanity.com/paper/2502.15218/) (2025)
- [DIFFA: Large Language Diffusion Models Can Listen and Understand](https://mlsanity.com/paper/2507.18452/) (2025)
- [All That Glitters Is Not Audio: Rethinking Text Priors and Audio Reliance in Audio-Language Evaluation](https://mlsanity.com/paper/2604.24401/) (2026)
- [From Alignment to Advancement: Bootstrapping Audio-Language Alignment with Synthetic Data](https://mlsanity.com/paper/2505.20166/) (2025)
- [Hearing to Translate: The Effectiveness of Speech Modality Integration into LLMs](https://mlsanity.com/paper/2512.16378/) (2025)
- [SAKE: Towards Editing Auditory Attribute Knowledge of Large Audio-Language Models](https://mlsanity.com/paper/2510.16917/) (2025)
- [Evaluating Emotion Recognition in Spoken Language Models on Emotionally Incongruent Speech](https://mlsanity.com/paper/2510.25054/) (2025)
- [Causal Tracing of Audio-Text Fusion in Large Audio Language Models](https://mlsanity.com/paper/2603.13768/) (2026)
- [Incorporating Contextual Paralinguistic Understanding in Large Speech-Language Models](https://mlsanity.com/paper/2508.07273/) (2025)
- [Investigating Safety Vulnerabilities of Large Audio-Language Models Under Speaker Emotional Variations](https://mlsanity.com/paper/2510.16893/) (2025)
- [Towards Reliable Large Audio Language Model](https://mlsanity.com/paper/2505.19294/) (2025)
- [ALAS: An Automatic Latent Alignment Score for Audio Language Models](https://mlsanity.com/paper/2505.19937/) (2025)
- [AzeroS: Extending LLM to Speech with Self-Generated Instruction-Free Tuning](https://mlsanity.com/paper/2601.06086/) (2025)
- [SCENEBench: An Audio Understanding Benchmark Grounded in Assistive and Industrial Use Cases](https://mlsanity.com/paper/2603.09853/) (2026)
- [ASPIRin: Action Space Projection for Interactivity-Optimized Reinforcement Learning in Full-Duplex Speech Language Models](https://mlsanity.com/paper/2604.10065/) (2026)
- [GOAT-SLM: A Spoken Language Model with Paralinguistic and Speaker Characteristic Awareness](https://mlsanity.com/paper/2507.18119/) (2025)

## Abstract (arXiv)

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 often undergo extensive speech instruction-tuning to bridge the gap between speech and text modalities. This requires significant annotation efforts and risks catastrophic forgetting of the original language capabilities. In this work, we present a simple yet effective automatic process for creating speech-text pair data that carefully injects speech paralinguistic understanding abilities into SLMs while preserving the inherent language capabilities of the text-based LLM. Our model demonstrates general capabilities for speech-related tasks without the need for speech instruction-tuning data, achieving impressive performance on Dynamic-SUPERB and AIR-Bench-Chat benchmarks. Furthermore, our model exhibits the ability to follow complex instructions derived from LLMs, such as specific output formatting and chain-of-thought reasoning. Our approach not only enhances the versatility and effectiveness of SLMs but also reduces reliance on extensive annotated datasets, paving the way for more efficient and capable speech understanding systems.