# TARQ: Tail-Aware Reconstruction Quantization for Rare-Word Robust Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2605.27808 · HTML: https://mlsanity.com/paper/2605.27808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [A Survey of Quantization Methods for Efficient Neural Network Inference](https://mlsanity.com/paper/2103.13630/) (2021)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks](https://mlsanity.com/paper/2402.04396/) (2024)
- [SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression](https://mlsanity.com/paper/2306.03078/) (2023)
- [Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion](https://mlsanity.com/paper/2104.02194/) (2021)
- [QTIP: Quantization with Trellises and Incoherence Processing](https://mlsanity.com/paper/2406.11235/) (2024)
- [SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition](https://mlsanity.com/paper/2104.02014/) (2021)
- [Tree-constrained Pointer Generator for End-to-end Contextual Speech Recognition](https://mlsanity.com/paper/2109.00627/) (2021)
- [Integer-only Zero-shot Quantization for Efficient Speech Recognition](https://mlsanity.com/paper/2103.16827/) (2021)
- [ContextASR-Bench: A Massive Contextual Speech Recognition Benchmark](https://mlsanity.com/paper/2507.05727/) (2025)
- [Minimising Biasing Word Errors for Contextual ASR with the Tree-Constrained Pointer Generator](https://mlsanity.com/paper/2205.09058/) (2022)
- [Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models](https://mlsanity.com/paper/2507.07877/) (2025)
- [StableQuant: Layer Adaptive Post-Training Quantization for Speech Foundation Models](https://mlsanity.com/paper/2504.14915/) (2025)

## Abstract (arXiv)

Data-aware post-training quantization (PTQ) minimizes a per-token reconstruction loss on a small calibration corpus, implicitly weighting positions by their empirical frequency. For Automatic Speech Recognition (ASR), this misaligns with tail-sensitive risk: names, numerals, and domain-specific words receive proportionally little calibration mass. We propose Tail-Aware Reconstruction Quantization (TARQ), a label-free PTQ framework that shifts calibration toward the lexical tail via rareBAL, a closed-form per-Linear-layer rule equalizing common/tail mass, paired with a metric-consistent residual correction. TARQ requires no entity labels, no curated calibration set, no validation decoding, and no additional training. Across eight ASR backbones and six datasets at W4G128, TARQ improves mean rare-Word Error Rate (rare-WER) without an aggregate-WER regression, achieves the lowest cross-corpus rare-WER swing among compared methods, and transfers to entity-rich benchmarks (ProfASR, ContextASR-Speech-En) without entity supervision.