# Pushing the performances of ASR models on English and Spanish accents

arXiv: https://arxiv.org/abs/2212.12048 · HTML: https://mlsanity.com/paper/2212.12048/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 22 references that are in the map.
- First posted on arXiv 22 Dec 2022.

## Builds on (most cited first, 18 of 18)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Unsupervised Cross-lingual Representation Learning for Speech Recognition](https://mlsanity.com/paper/2006.13979/) (2020)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [Self-Training for End-to-End Speech Recognition](https://mlsanity.com/paper/1909.09116/) (2019)
- [Emformer: Efficient Memory Transformer Based Acoustic Model For Low Latency Streaming Speech Recognition](https://mlsanity.com/paper/2010.10759/) (2020)
- [Massively Multilingual ASR: 50 Languages, 1 Model, 1 Billion Parameters](https://mlsanity.com/paper/2007.03001/) (2020)
- [Rethinking Evaluation in ASR: Are Our Models Robust Enough?](https://mlsanity.com/paper/2010.11745/) (2020)
- [Joint Unsupervised and Supervised Training for Multilingual ASR](https://mlsanity.com/paper/2111.08137/) (2021)
- [Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory](https://mlsanity.com/paper/2005.08042/) (2020)
- [Improving RNN Transducer Based ASR with Auxiliary Tasks](https://mlsanity.com/paper/2011.03109/) (2020)
- [Joint Masked CPC and CTC Training for ASR](https://mlsanity.com/paper/2011.00093/) (2020)
- [Joint Modeling of Accents and Acoustics for Multi-Accent Speech Recognition](https://mlsanity.com/paper/1802.02656/) (2018)
- [ASR4REAL: An extended benchmark for speech models](https://mlsanity.com/paper/2110.08583/) (2021)
- [Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings](https://mlsanity.com/paper/2110.03520/) (2021)
- [NISP: A Multi-lingual Multi-accent Dataset for Speaker Profiling](https://mlsanity.com/paper/2007.06021/) (2020)

## Built on by (most cited first, 1 of 1)

- [Dialect and Gender Bias in YouTube's Spanish Captioning System](https://mlsanity.com/paper/2602.24002/) (2026)

## Abstract (arXiv)

Speech to text models tend to be trained and evaluated against a single target accent. This is especially true for English for which native speakers from the United States became the main benchmark. In this work, we are going to show how two simple methods: pre-trained embeddings and auxiliary classification losses can improve the performance of ASR systems. We are looking for upgrades as universal as possible and therefore we will explore their impact on several models architectures and several languages.