# Performance evaluation of SLAM-ASR: The Good, the Bad, the Ugly, and the Way Forward

arXiv: https://arxiv.org/abs/2411.03866 · HTML: https://mlsanity.com/paper/2411.03866/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 28 references that are in the map.
- First posted on arXiv 6 Nov 2024.

## Builds on (most cited first, 20 of 20)

- [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)
- [MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models](https://mlsanity.com/paper/2304.10592/) (2023)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SALMONN: Towards Generic Hearing Abilities for Large Language Models](https://mlsanity.com/paper/2310.13289/) (2023)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [On decoder-only architecture for speech-to-text and large language model integration](https://mlsanity.com/paper/2307.03917/) (2023)
- [Prompting Large Language Models with Speech Recognition Abilities](https://mlsanity.com/paper/2307.11795/) (2023)
- [An Embarrassingly Simple Approach for LLM with Strong ASR Capacity](https://mlsanity.com/paper/2402.08846/) (2024)
- [Generative Speech Recognition Error Correction with Large Language Models and Task-Activating Prompting](https://mlsanity.com/paper/2309.15649/) (2023)
- [SpeechVerse: A Large-scale Generalizable Audio Language Model](https://mlsanity.com/paper/2405.08295/) (2024)
- [Can Generative Large Language Models Perform ASR Error Correction?](https://mlsanity.com/paper/2307.04172/) (2023)
- [Prompting Large Language Models for Zero-Shot Domain Adaptation in Speech Recognition](https://mlsanity.com/paper/2306.16007/) (2023)
- [Revisiting Large Language Models as Zero-shot Relation Extractors](https://mlsanity.com/paper/2310.05028/) (2023)
- [BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications](https://mlsanity.com/paper/2110.05781/) (2021)
- [Multilingual and Fully Non-Autoregressive ASR with Large Language Model Fusion: A Comprehensive Study](https://mlsanity.com/paper/2401.12789/) (2024)
- [A two-step approach to leverage contextual data: speech recognition in air-traffic communications](https://mlsanity.com/paper/2202.03725/) (2022)
- [WHISMA: A Speech-LLM to Perform Zero-shot Spoken Language Understanding](https://mlsanity.com/paper/2408.16423/) (2024)

## Built on by (most cited first, 17 of 17)

- [Text-only adaptation in LLM-based ASR through text denoising](https://mlsanity.com/paper/2601.20900/) (2026)
- [Low-Resource Domain Adaptation for Speech LLMs via Text-Only Fine-Tuning](https://mlsanity.com/paper/2506.05671/) (2025)
- [Speech LLMs in Low-Resource Scenarios: Data Volume Requirements and the Impact of Pretraining on High-Resource Languages](https://mlsanity.com/paper/2508.05149/) (2025)
- [Approaching Dialogue State Tracking via Aligning Speech Encoders and LLMs](https://mlsanity.com/paper/2506.08633/) (2025)
- [The Eloquence team submission for task 1 of MLC-SLM challenge](https://mlsanity.com/paper/2507.19308/) (2025)
- [Do LLM Decoders Listen Fairly? Benchmarking How Language Model Priors Shape Bias in Speech Recognition](https://mlsanity.com/paper/2604.21276/) (2026)
- [Adapting Automatic Speech Recognition for Accented Air Traffic Control Communications](https://mlsanity.com/paper/2502.20311/) (2025)
- [Optimal Transport Regularization for Speech Text Alignment in Spoken Language Models](https://mlsanity.com/paper/2508.08131/) (2025)
- [Language Family Matters: Evaluating LLM-Based ASR Across Linguistic Boundaries](https://mlsanity.com/paper/2601.18899/) (2026)
- [Reducing Prompt Sensitivity in LLM-based Speech Recognition Through Learnable Projection](https://mlsanity.com/paper/2601.20898/) (2026)
- [Enhancing Multilingual LLM-based ASR with Mixture of Experts and Dynamic Downsampling](https://mlsanity.com/paper/2606.10439/) (2026)
- [MEUSLI: a Multilingual Projector for LLM-based ASR and Beyond](https://mlsanity.com/paper/2607.22100/) (2026)
- [TokenVerse++: Towards Flexible Multitask Learning with Dynamic Task Activation](https://mlsanity.com/paper/2508.19856/) (2025)
- [Measuring the Redundancy of Decoder Layers in SpeechLLMs](https://mlsanity.com/paper/2603.05121/) (2026)
- [When Demonstrations Fail: Diagnosing the Limits of In-Context Learning in Large Audio-Language Models with Progressive Cue Removal](https://mlsanity.com/paper/2603.20433/) (2026)
- [Closing the Speech-Text Gap with Limited Audio for Effective Domain Adaptation in LLM-Based ASR](https://mlsanity.com/paper/2604.06487/) (2026)
- [LuxSQA: Ask Me in Luxembourgish with TTS-Augmented Spoken Question Answering](https://mlsanity.com/paper/2607.02763/) (2026)

## Abstract (arXiv)

Recent research has demonstrated that training a linear connector between speech foundation encoders and large language models (LLMs) enables this architecture to achieve strong ASR capabilities. Despite the impressive results, it remains unclear whether these simple approaches are robust enough across different scenarios and speech conditions, such as domain shifts and speech perturbations. In this paper, we address these questions by conducting various ablation experiments using a recent and widely adopted approach called SLAM-ASR. We present novel empirical findings that offer insights on how to effectively utilize the SLAM-ASR architecture across a wide range of settings. Our main findings indicate that SLAM-ASR exhibits poor performance in cross-domain evaluation settings. Additionally, speech perturbations on in-domain data, such as changes in speech rate or additive noise, can significantly degrade performance. Our findings offer critical insights for fine-tuning and configuring robust LLM-based ASR models, tailored to different data characteristics and computational resources.