Distilling an End-to-End Voice Assistant Without Instruction Training Data (2025.acl-long)
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| Challenge: | Recent efforts to train speech-only LLMs have led to models “forging” speech information from text-only models. |
| Approach: | They propose a paradigm for training Speech Large Language Models without instruction data by using the response of a text-only LLM to transcripts as self-supervision. |
| Outcome: | The proposed model generalizes to Spoken Question Answering, Classification, and Translation and achieves a 72% win rate compared with state-of-the-art models like Qwen 2 Audio . |
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| Challenge: | Existing LLMs lack datasets and biased training tasks to follow speech instructions. |
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Juanhui Li, Sreyashi Nag, Hui Liu, Xianfeng Tang, Sheikh Muhammad Sarwar, Limeng Cui, Hansu Gu, Suhang Wang, Qi He, Jiliang Tang
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| Challenge: | Existing approaches to end-to-end speech translation (E2E) models only allow one way knowledge transfer, which is limited by the performance of the teacher model. |
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AudioChatLlama: Towards General-Purpose Speech Abilities for LLMs (2024.naacl-long)
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Yassir Fathullah, Chunyang Wu, Egor Lakomkin, Ke Li, Junteng Jia, Yuan Shangguan, Jay Mahadeokar, Ozlem Kalinli, Christian Fuegen, Mike Seltzer
| Challenge: | a new model for speech processing and reasoning uses curated data instead of text. |
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| Challenge: | Latent Synthesis is an efficient textual data utilization framework for end-to-end speech processing models . labeled speech data are scarcer and more expensive for collection compared to textual ones . |
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Contrastive Learning for Task-Independent SpeechLLM-Pretraining (2025.findings-acl)
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| Challenge: | Large language models excel in speech processing tasks but their reliance on written text limits their application in real-world scenarios. |
| Approach: | They propose a task-independent speech pretraining stage and task-specific fine-tuning stage to adapt LLMs to speech processing tasks. |
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Consistency is Key: On Data-Efficient Modality Transfer in Speech Translation (2023.findings-emnlp)
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| Challenge: | End-to-end approaches to speech translation suffer from data scarcity compared to machine translation (MT). |
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| Challenge: | Large language models (LLMs) have demonstrated state-of-the-art (SOTA) performance across a wide spectrum of tasks. |
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Self-Powered LLM Modality Expansion for Large Speech-Text Models (2024.emnlp-main)
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| Challenge: | Large language models exhibit remarkable performance across diverse tasks . however, these methods require significant resource demands and tend to overfit specific tasks. |
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VoiceTextBlender: Augmenting Large Language Models with Speech Capabilities via Single-Stage Joint Speech-Text Supervised Fine-Tuning (2025.naacl-long)
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Yifan Peng, Krishna C Puvvada, Zhehuai Chen, Piotr Zelasko, He Huang, Kunal Dhawan, Ke Hu, Shinji Watanabe, Jagadeesh Balam, Boris Ginsburg
| Challenge: | Recent studies have augmented large language models (LLMs) with speech capabilities, leading to the development of speech language models. |
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