# Index-ASR Technical Report

arXiv: https://arxiv.org/abs/2601.00890 · HTML: https://mlsanity.com/paper/2601.00890/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Dec 2025.

## Built on by (most cited first, 5 of 5)

- [Uni-ASR: Unified LLM-Based Architecture for Non-Streaming and Streaming Automatic Speech Recognition](https://mlsanity.com/paper/2603.11123/) (2026)
- [Speech LLMs are Contextual Reasoning Transcribers](https://mlsanity.com/paper/2604.00610/) (2026)
- [NIM4-ASR: Towards Efficient, Robust, and Customizable Real-Time LLM-Based ASR](https://mlsanity.com/paper/2604.18105/) (2026)
- [Rethinking Speech-LLM Integration for ASR: Effective Joint Speech-Text Training by Interleaving](https://mlsanity.com/paper/2607.01733/) (2026)
- [ScreenASR-Bench: A Tiered Benchmark for Speech Recognition with Visual Context](https://mlsanity.com/paper/2610.16260/) (2026)

## Abstract (arXiv)

Automatic speech recognition (ASR) has witnessed remarkable progress in recent years, largely driven by the emergence of LLM-based ASR paradigm. Despite their strong performance on a variety of open-source benchmarks, existing LLM-based ASR systems still suffer from two critical limitations. First, they are prone to hallucination errors, often generating excessively long and repetitive outputs that are not well grounded in the acoustic input. Second, they provide limited support for flexible and fine-grained contextual customization. To address these challenges, we propose Index-ASR, a large-scale LLM-based ASR system designed to simultaneously enhance robustness and support customizable hotword recognition. The core idea of Index-ASR lies in the integration of LLM and large-scale training data enriched with background noise and contextual information. Experimental results show that our Index-ASR achieves strong performance on both open-source benchmarks and in-house test sets, highlighting its robustness and practicality for real-world ASR applications.