# Unveiling the Potential of LLM-Based ASR on Chinese Open-Source Datasets

arXiv: https://arxiv.org/abs/2405.02132 · HTML: https://mlsanity.com/paper/2405.02132/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 39 references that are in the map.
- First posted on arXiv 3 May 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [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)
- [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)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face](https://mlsanity.com/paper/2303.17580/) (2023)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities](https://mlsanity.com/paper/2305.11000/) (2023)
- [SALMONN: Towards Generic Hearing Abilities for Large Language Models](https://mlsanity.com/paper/2310.13289/) (2023)
- [BEATs: Audio Pre-Training with Acoustic Tokenizers](https://mlsanity.com/paper/2212.09058/) (2022)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head](https://mlsanity.com/paper/2304.12995/) (2023)
- [Listen, Think, and Understand](https://mlsanity.com/paper/2305.10790/) (2023)
- [WenetSpeech: A 10000+ Hours Multi-domain Mandarin Corpus for Speech Recognition](https://mlsanity.com/paper/2110.03370/) (2021)
- [On decoder-only architecture for speech-to-text and large language model integration](https://mlsanity.com/paper/2307.03917/) (2023)
- [Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition](https://mlsanity.com/paper/2206.08317/) (2022)
- [AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale](https://mlsanity.com/paper/1808.10583/) (2018)
- [Language Modeling with Deep Transformers](https://mlsanity.com/paper/1905.04226/) (2019)
- [An Embarrassingly Simple Approach for LLM with Strong ASR Capacity](https://mlsanity.com/paper/2402.08846/) (2024)
- [AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario](https://mlsanity.com/paper/2104.03603/) (2021)
- [Can Generative Large Language Models Perform ASR Error Correction?](https://mlsanity.com/paper/2307.04172/) (2023)
- [WeNet 2.0: More Productive End-to-End Speech Recognition Toolkit](https://mlsanity.com/paper/2203.15455/) (2022)
- [U2++: Unified Two-pass Bidirectional End-to-end Model for Speech Recognition](https://mlsanity.com/paper/2106.05642/) (2021)
- [Can ChatGPT Detect Intent? Evaluating Large Language Models for Spoken Language Understanding](https://mlsanity.com/paper/2305.13512/) (2023)
- [Leveraging Large Language Models for Exploiting ASR Uncertainty](https://mlsanity.com/paper/2309.04842/) (2023)
- [Private Language Model Adaptation for Speech Recognition](https://mlsanity.com/paper/2110.10026/) (2021)
- [SpeechColab Leaderboard: An Open-Source Platform for Automatic Speech Recognition Evaluation](https://mlsanity.com/paper/2403.08196/) (2024)

## Built on by (most cited first, 13 of 13)

- [Audio-CoT: Exploring Chain-of-Thought Reasoning in Large Audio Language Model](https://mlsanity.com/paper/2501.07246/) (2025)
- [Advancing Multi-talker ASR Performance with Large Language Models](https://mlsanity.com/paper/2408.17431/) (2024)
- [Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM](https://mlsanity.com/paper/2409.15905/) (2024)
- [Text-only adaptation in LLM-based ASR through text denoising](https://mlsanity.com/paper/2601.20900/) (2026)
- [Leveraging LLM and Self-Supervised Training Models for Speech Recognition in Chinese Dialects: A Comparative Analysis](https://mlsanity.com/paper/2505.21138/) (2025)
- [Towards Reliable Large Audio Language Model](https://mlsanity.com/paper/2505.19294/) (2025)
- [Enhancing Multimodal Sentiment Analysis for Missing Modality through Self-Distillation and Unified Modality Cross-Attention](https://mlsanity.com/paper/2410.15029/) (2024)
- [Reducing Prompt Sensitivity in LLM-based Speech Recognition Through Learnable Projection](https://mlsanity.com/paper/2601.20898/) (2026)
- [A Comparative Study of LLM-based ASR and Whisper in Low Resource and Code Switching Scenario](https://mlsanity.com/paper/2412.00721/) (2024)
- [SHNU Multilingual Conversational Speech Recognition System for INTERSPEECH 2025 MLC-SLM Challenge](https://mlsanity.com/paper/2507.03343/) (2025)
- [MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond](https://mlsanity.com/paper/2607.22100/) (2026)
- [Serial-Parallel Dual-Path Architecture for Speaking Style Recognition](https://mlsanity.com/paper/2510.11732/) (2025)
- [Dual-Form ASR: Semantics-Aware Inverse Text Normalization for Chinese Speech Recognition](https://mlsanity.com/paper/2609.02901/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated unparalleled effectiveness in various NLP tasks, and integrating LLMs with automatic speech recognition (ASR) is becoming a mainstream paradigm. Building upon this momentum, our research delves into an in-depth examination of this paradigm on a large open-source Chinese dataset. Specifically, our research aims to evaluate the impact of various configurations of speech encoders, LLMs, and projector modules in the context of the speech foundation encoder-LLM ASR paradigm. Furthermore, we introduce a three-stage training approach, expressly developed to enhance the model's ability to align auditory and textual information. The implementation of this approach, alongside the strategic integration of ASR components, enabled us to achieve the SOTA performance on the AISHELL-1, Test_Net, and Test_Meeting test sets. Our analysis presents an empirical foundation for future research in LLM-based ASR systems and offers insights into optimizing performance using Chinese datasets. We will publicly release all scripts used for data preparation, training, inference, and scoring, as well as pre-trained models and training logs to promote reproducible research.