# Accurate and Structured Pruning for Efficient Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2305.19549 · HTML: https://mlsanity.com/paper/2305.19549/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 31 references that are in the map.
- First posted on arXiv 31 May 2023.

## Builds on (most cited first, 24 of 24)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation](https://mlsanity.com/paper/1308.3432/) (2013)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned](https://mlsanity.com/paper/1905.09418/) (2019)
- [Are Sixteen Heads Really Better than One?](https://mlsanity.com/paper/1905.10650/) (2019)
- [Learning Sparse Neural Networks through L0 Regularization](https://mlsanity.com/paper/1712.01312/) (2017)
- [Importance Estimation for Neural Network Pruning](https://mlsanity.com/paper/1906.10771/) (2019)
- [Movement Pruning: Adaptive Sparsity by Fine-Tuning](https://mlsanity.com/paper/2005.07683/) (2020)
- [Structured Pruning of Large Language Models](https://mlsanity.com/paper/1910.04732/) (2019)
- [Block Pruning For Faster Transformers](https://mlsanity.com/paper/2109.04838/) (2021)
- [Structured Pruning Learns Compact and Accurate Models](https://mlsanity.com/paper/2204.00408/) (2022)
- [DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT](https://mlsanity.com/paper/2110.01900/) (2021)
- [PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition](https://mlsanity.com/paper/2106.05933/) (2021)
- [PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation](https://mlsanity.com/paper/2106.14681/) (2021)
- [PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations](https://mlsanity.com/paper/2203.16965/) (2022)
- [Multi-stage Progressive Compression of Conformer Transducer for On-device Speech Recognition](https://mlsanity.com/paper/2210.00169/) (2022)
- [On Compressing Sequences for Self-Supervised Speech Models](https://mlsanity.com/paper/2210.07189/) (2022)
- [Improving generalizability of distilled self-supervised speech processing models under distorted settings](https://mlsanity.com/paper/2210.07978/) (2022)
- [SwiftPruner: Reinforced Evolutionary Pruning for Efficient Ad Relevance](https://mlsanity.com/paper/2209.00625/) (2022)
- [Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition](https://mlsanity.com/paper/2210.15631/) (2022)

## Built on by (most cited first, 11 of 11)

- [A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and Recommendations](https://mlsanity.com/paper/2308.06767/) (2023)
- [CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders](https://mlsanity.com/paper/2309.07707/) (2023)
- [Dynamic Encoder Size Based on Data-Driven Layer-wise Pruning for Speech Recognition](https://mlsanity.com/paper/2407.18930/) (2024)
- [Efficient and Robust Speaker Diarization via Structured Pruning of Self-Supervised Models](https://mlsanity.com/paper/2506.18623/) (2025)
- [Effective and Efficient Mixed Precision Quantization of Speech Foundation Models](https://mlsanity.com/paper/2501.03643/) (2025)
- [One-pass Multiple Conformer and Foundation Speech Systems Compression and Quantization Using An All-in-one Neural Model](https://mlsanity.com/paper/2406.10160/) (2024)
- [Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression](https://mlsanity.com/paper/2501.18895/) (2025)
- [Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates](https://mlsanity.com/paper/2505.22608/) (2025)
- [Spiking and Event-driven Neuromorphic Mamba Models for Efficient Speech Recognition](https://mlsanity.com/paper/2606.01135/) (2026)
- [Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering](https://mlsanity.com/paper/2606.11836/) (2026)
- [VAD to the Bone: Ultra-Tiny Speech Activity Detection for Edge Deployment](https://mlsanity.com/paper/2607.25870/) (2026)

## Abstract (arXiv)

Automatic Speech Recognition (ASR) has seen remarkable advancements with deep neural networks, such as Transformer and Conformer. However, these models typically have large model sizes and high inference costs, posing a challenge to deploy on resource-limited devices. In this paper, we propose a novel compression strategy that leverages structured pruning and knowledge distillation to reduce the model size and inference cost of the Conformer model while preserving high recognition performance. Our approach utilizes a set of binary masks to indicate whether to retain or prune each Conformer module, and employs L0 regularization to learn the optimal mask values. To further enhance pruning performance, we use a layerwise distillation strategy to transfer knowledge from unpruned to pruned models. Our method outperforms all pruning baselines on the widely used LibriSpeech benchmark, achieving a 50% reduction in model size and a 28% reduction in inference cost with minimal performance loss.