mlsanityData to 14 Oct 2026

StableQuant: Layer Adaptive Post-Training Quantization for Speech Foundation Models

arXiv 2504.14915 ↗First posted 21 Apr 2025eess.AS cs.AIcited by 3

Not on the map. Why?

Abstract (arXiv)

In this paper, we propose StableQuant, a novel adaptive post-training quantization (PTQ) algorithm for widely used speech foundation models (SFMs). While PTQ has been successfully employed for compressing large language models (LLMs) due to its ability to bypass additional fine-tuning, directly applying these techniques to SFMs may not yield optimal results, as SFMs utilize distinct network architecture for feature extraction. StableQuant demonstrates optimal quantization performance regardless of the network architecture type, as it adaptively determines the quantization range for each layer by analyzing both the scale distributions and overall performance. We evaluate our algorithm on two SFMs, HuBERT and wav2vec2.0, for an automatic speech recognition (ASR) task, and achieve superior performance compared to traditional PTQ methods. StableQuant successfully reduces the sizes of SFM models to a quarter and doubles the inference speed while limiting the word error rate (WER) performance drop to less than 0.3% with 8-bit quantization.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 22 of its 26 references that are in the map. First posted on arXiv 21 Apr 2025.

Builds on

22 of 26 references.

References · 22

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  4. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  5. 2021WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processingcited by 1,657
  6. 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,386
  7. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
  8. 2021SoundStream: An End-to-End Neural Audio Codeccited by 771
  9. 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 666
  10. 2020Up or Down? Adaptive Rounding for Post-Training Quantizationcited by 621
  11. 2018Quantizing deep convolutional networks for efficient inference: A whitepapercited by 521
  12. 2021A White Paper on Neural Network Quantizationcited by 472
10 more
  1. 2019Libri-Light: A Benchmark for ASR with Limited or No Supervisioncited by 467
  2. 2024The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bitscited by 308
  3. 2020ZeroQ: A Novel Zero Shot Quantization Frameworkcited by 269
  4. 2022Self-Supervised Speech Representation Learning: A Reviewcited by 260
  5. 2020Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluationcited by 188
  6. 2023Speak, Read and Prompt: High-Fidelity Text-to-Speech with Minimal Supervisioncited by 169
  7. 2021DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERTcited by 144
  8. 2022LightHuBERT: Lightweight and Configurable Speech Representation Learning with Once-for-All Hidden-Unit BERTcited by 50
  9. 2022Investigating self-supervised learning for speech enhancement and separationcited by 35
  10. 2023Structured Pruning of Self-Supervised Pre-trained Models for Speech Recognition and Understandingcited by 31

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Diagnostic-Driven Layer-Wise Compensation for Post-Training Quantization of Encoder-Decoder ASR Modelscited by 0
  2. 2026TARQ: Tail-Aware Reconstruction Quantization for Rare-Word Robust Automatic Speech Recognitioncited by 0
  3. 2026Efficiency-Performance Trade-offs in Neural Speaker Diarization via Structured Pruning and Low-Bit Quantizationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Layer-Wise High-Impact Parameter Ratio Optimization in Post-Training Quantization for Large Language Models
  2. 2026SliderQuant: Accurate Post-Training Quantization for LLMs
  3. 2026ReQuant: Fixed-Grid Discrete Refinement for Post-Training Quantization
  4. 2023ZeroQuant-V2: Exploring Post-training Quantization in LLMs from Comprehensive Study to Low Rank Compensation
  5. 2025Benchmarking Post-Training Quantization in LLMs: Comprehensive Taxonomy, Unified Evaluation, and Comparative Analysis
  6. 2025PTQ1.61: Push the Real Limit of Extremely Low-Bit Post-Training Quantization Methods for Large Language Models
  7. 2023QLLM: Accurate and Efficient Low-Bitwidth Quantization for Large Language Models
  8. 2025Quantization Error Propagation: Revisiting Layer-Wise Post-Training Quantization