# FeaRLESS: Feature Refinement Loss for Ensembling Self-Supervised Learning Features in Robust End-to-end Speech Recognition

arXiv: https://arxiv.org/abs/2206.15056 · HTML: https://mlsanity.com/paper/2206.15056/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 15 of 15)

- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [ESPnet: End-to-End Speech Processing Toolkit](https://mlsanity.com/paper/1804.00015/) (2018)
- [TERA: Self-Supervised Learning of Transformer Encoder Representation for Speech](https://mlsanity.com/paper/2007.06028/) (2020)
- [An Unsupervised Autoregressive Model for Speech Representation Learning](https://mlsanity.com/paper/1904.03240/) (2019)
- [Reducing Overfitting in Deep Networks by Decorrelating Representations](https://mlsanity.com/paper/1511.06068/) (2015)
- [Self-supervised learning of visual features through embedding images into text topic spaces](https://mlsanity.com/paper/1705.08631/) (2017)
- [An Exploration of Self-Supervised Pretrained Representations for End-to-End Speech Recognition](https://mlsanity.com/paper/2110.04590/) (2021)
- [Face Detection using Deep Learning: An Improved Faster RCNN Approach](https://mlsanity.com/paper/1701.08289/) (2017)
- [On Architectures and Training for Raw Waveform Feature Extraction in ASR](https://mlsanity.com/paper/2104.04298/) (2021)
- [FEARLESS STEPS Challenge (FS-2): Supervised Learning with Massive Naturalistic Apollo Data](https://mlsanity.com/paper/2008.06764/) (2020)
- [Scenario Aware Speech Recognition: Advancements for Apollo Fearless Steps & CHiME-4 Corpora](https://mlsanity.com/paper/2109.11086/) (2021)

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

- [Findings of the 2023 ML-SUPERB Challenge: Pre-Training and Evaluation over More Languages and Beyond](https://mlsanity.com/paper/2310.05513/) (2023)
- [High-Quality Automatic Voice Over with Accurate Alignment: Supervision through Self-Supervised Discrete Speech Units](https://mlsanity.com/paper/2306.17005/) (2023)
- [Mind the Shift: Using Delta SSL Embeddings to Enhance Child ASR](https://mlsanity.com/paper/2601.20142/) (2026)
- [Density Adaptive Attention is All You Need: Robust Parameter-Efficient Fine-Tuning Across Multiple Modalities](https://mlsanity.com/paper/2401.11143/) (2024)
- [Converting Anyone's Voice: End-to-End Expressive Voice Conversion with a Conditional Diffusion Model](https://mlsanity.com/paper/2405.01730/) (2024)

## Abstract (arXiv)

Self-supervised learning representations (SSLR) have resulted in robust features for downstream tasks in many fields. Recently, several SSLRs have shown promising results on automatic speech recognition (ASR) benchmark corpora. However, previous studies have only shown performance for solitary SSLRs as an input feature for ASR models. In this study, we propose to investigate the effectiveness of diverse SSLR combinations using various fusion methods within end-to-end (E2E) ASR models. In addition, we will show there are correlations between these extracted SSLRs. As such, we further propose a feature refinement loss for decorrelation to efficiently combine the set of input features. For evaluation, we show that the proposed 'FeaRLESS learning features' perform better than systems without the proposed feature refinement loss for both the WSJ and Fearless Steps Challenge (FSC) corpora.