VocalNet: Speech LLMs with Multi-Token Prediction for Faster and High-Quality Generation (2025.emnlp-main)
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| Challenge: | Experimental results show VocalNet outperforms existing open-source speech LLMs despite limited training data. |
| Approach: | They propose a scalable and model-agnostic training framework and a novel multi-token prediction paradigm for speech generation. |
| Outcome: | The proposed model outperforms open-source speech LLMs while outperforming existing open-sourced models. |
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| Challenge: | Multi-token prediction (MTP) is a pre-training objective for language models . prior work has shown that smaller language models struggle with the MTP objective . |
| Approach: | They propose a curriculum learning strategy that uses multiple prediction heads to predict the next tokens at each prediction step. |
| Outcome: | The proposed curriculum improves performance and output quality while retaining the benefits of self-speculative decoding. |
LLM-Codec: Neural Audio Codec Meets Language Model Objectives (2026.findings-acl)
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| Challenge: | Neural audio codecs are optimized for waveform reconstruction rather than autoregressive prediction. |
| Approach: | They propose to augment codec training with language-model-facing objectives while keeping both codec and LLM architectures unchanged. |
| Outcome: | The proposed model improves speech coherence and predictability by preserving the semantic alignment between audio and text representations. |
Generative Pre-trained Speech Language Model with Efficient Hierarchical Transformer (2024.acl-long)
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| Challenge: | Experimental results indicate that GPST significantly outperforms the existing speech language models in terms of word error rate, speech quality, and speaker similarity. |
| Approach: | They propose a hierarchical transformer that quantizes audio waveforms into two distinct types of discrete speech representations and integrates them within a transformer architecture. |
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MM-LLMs: Recent Advances in MultiModal Large Language Models (2024.findings-acl)
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| Challenge: | MultiModal Large Language Models (MM-LLMs) have undergone significant advances in the past year . traditional MM models incur substantial computational costs, especially when trained from scratch . |
| Approach: | They propose a taxonomy encompassing 126 MM-LLMs and summarize key training recipes to enhance their potency. |
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Make-A-Voice: Revisiting Voice Large Language Models as Scalable Multilingual and Multitask Learners (2024.acl-long)
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Rongjie Huang, Chunlei Zhang, Yongqi Wang, Dongchao Yang, Jinchuan Tian, Zhenhui Ye, Luping Liu, Zehan Wang, Ziyue Jiang, Xuankai Chang, Jiatong Shi, Chao Weng, Zhou Zhao, Dong Yu
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SimulS2S-LLM: Unlocking Simultaneous Inference of Speech LLMs for Speech-to-Speech Translation (2025.acl-long)
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| Challenge: | Simultaneous speech translation (SST) outputs translations in parallel with streaming speech input, balancing translation quality and latency. |
| Approach: | They propose to train LLMs offline and employ a test-time policy to guide simultaneous inference by extracting boundary-aware speech prompts that allow it to be better matched with text input data. |
| Outcome: | The proposed model trains speech LLMs offline and employs a test-time policy to guide simultaneous inference. |
LLMs for Low Resource Languages in Multilingual, Multimodal and Dialectal Settings (2024.eacl-tutorials)
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| Challenge: | Recent advances in AI can be attributed to the remarkable performance of Large Language Models (LLMs) success of LLMs depends on specific training techniques, such as instruction tuning and prompting . |
| Approach: | They explore the capabilities of Large Language Models (LLMs) in various tasks and languages . they also examine their performance, fine-tuning, instructions tuning, and close vs. open models . |
| Outcome: | The proposed model can be used for speech and multimodal tasks across modalities, languages, and dialects. |
When Large Language Models Meet Speech: A Survey on Integration Approaches (2025.findings-acl)
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| Challenge: | Recent advances in large language models have spurred interest in expanding their application beyond text-based tasks. |
| Approach: | They propose to categorize the integration of speech with LLMs into three main approaches . they demonstrate how these methods are applied across various speech-related applications . |
| Outcome: | The proposed methods are applied across speech-related applications and highlight the challenges in this field to offer inspiration for future research. |
Recent Advances in Speech Language Models: A Survey (2025.acl-long)
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Wenqian Cui, Dianzhi Yu, Xiaoqi Jiao, Ziqiao Meng, Guangyan Zhang, Qichao Wang, Steven Y. Guo, Irwin King
| Challenge: | Text-based Large Language Models (LLMs) are a promising solution for end-to-end speech interaction. |
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| Outcome: | The survey offers an overview of recent approaches to building SpeechLMs . it outlines core architectural components, training methodologies, evaluation strategies and challenges . |
WavLLM: Towards Robust and Adaptive Speech Large Language Model (2024.findings-emnlp)
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Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen, Lingwei Meng, Hongkun Hao, Jing Pan, Xunying Liu, Jinyu Li, Sunit Sivasankaran, Linquan Liu, Furu Wei
| Challenge: | Recent advances in large language models (LLMs) have expanded their scope to encompass multimodal functions. |
| Approach: | They propose a robust and adaptive speech large language model with dual encoders . they validate the model on universal speech benchmarks and apply it to specialized speech-question-answer datasets based on a CoT approach . |
| Outcome: | The proposed model achieves state-of-the-art performance across a range of speech tasks on the same model size. |