{"total":23,"rows":[{"id":"2305.17651","title":"DPHuBERT: Joint Distillation and Pruning of Self-Supervised Speech Models","created":"2023-05-28","cited_by_count":36,"area":null},{"id":"2306.06672","title":"Reducing Barriers to Self-Supervised Learning: HuBERT Pre-training with Academic Compute","created":"2023-06-11","cited_by_count":21,"area":null},{"id":"2309.05472","title":"LeBenchmark 2.0: a Standardized, Replicable and Enhanced Framework for Self-supervised Representations of French Speech","created":"2023-09-11","cited_by_count":18,"area":null},{"id":"2401.17632","title":"What Do Self-Supervised Speech and Speaker Models Learn? New Findings From a Cross Model Layer-Wise Analysis","created":"2024-01-31","cited_by_count":11,"area":null},{"id":"2306.08374","title":"SpeechGLUE: How Well Can Self-Supervised Speech Models Capture Linguistic Knowledge?","created":"2023-06-14","cited_by_count":9,"area":null},{"id":"2309.07707","title":"CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders","created":"2023-09-14","cited_by_count":9,"area":null},{"id":"2211.06369","title":"Enhancing and Adversarial: Improve ASR with Speaker Labels","created":"2022-11-11","cited_by_count":6,"area":null},{"id":"2305.11685","title":"Recycle-and-Distill: Universal Compression Strategy for Transformer-based Speech SSL Models with Attention Map Reusing and Masking Distillation","created":"2023-05-19","cited_by_count":6,"area":null},{"id":"2501.03643","title":"Effective and Efficient Mixed Precision Quantization of Speech Foundation Models","created":"2025-01-07","cited_by_count":5,"area":null},{"id":"2305.05201","title":"Exploration of Language Dependency for Japanese Self-Supervised Speech Representation Models","created":"2023-05-09","cited_by_count":4,"area":null},{"id":"2406.07696","title":"Sustainable self-supervised learning for speech representations","created":"2024-06-11","cited_by_count":4,"area":null},{"id":"2406.10160","title":"One-pass Multiple Conformer and Foundation Speech Systems Compression and Quantization Using An All-in-one Neural Model","created":"2024-06-14","cited_by_count":4,"area":null},{"id":"2210.15631","title":"Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition","created":"2022-10-27","cited_by_count":3,"area":null},{"id":"2312.09040","title":"STaR: Distilling Speech Temporal Relation for Lightweight Speech Self-Supervised Learning Models","created":"2023-12-14","cited_by_count":3,"area":null},{"id":"2312.11142","title":"Efficiency-oriented approaches for self-supervised speech representation learning","created":"2023-12-18","cited_by_count":1,"area":null},{"id":"2501.18895","title":"Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression","created":"2025-01-31","cited_by_count":1,"area":null},{"id":"2505.22608","title":"Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates","created":"2025-05-28","cited_by_count":1,"area":null},{"id":"2308.12568","title":"A Small and Fast BERT for Chinese Medical Punctuation Restoration","created":"2023-08-24","cited_by_count":0,"area":null},{"id":"2311.02772","title":"Attention or Convolution: Transformer Encoders in Audio Language Models for Inference Efficiency","created":"2023-11-05","cited_by_count":0,"area":null},{"id":"2510.04241","title":"Diffusion-Assisted Distillation for Self-Supervised Graph Representation Learning with MLPs","created":"2025-10-05","cited_by_count":0,"area":null},{"id":"2604.14186","title":"HARNESS: Lightweight Distilled Arabic Speech Foundation Models","created":"2026-03-31","cited_by_count":0,"area":null},{"id":"2606.11766","title":"Fast Speech Foundation Model Distillation Using Interleaved Stacking","created":"2026-06-10","cited_by_count":0,"area":null},{"id":"2606.11836","title":"Towards Data-free and Training-free Compression for Speech Foundation Models Using Parameter Clustering","created":"2026-06-10","cited_by_count":0,"area":null}],"next_cursor":null}