# Data-Driven Mispronunciation Pattern Discovery for Robust Speech Recognition

arXiv: https://arxiv.org/abs/2502.00583 · HTML: https://mlsanity.com/paper/2502.00583/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 18 references that are in the map.
- First posted on arXiv 1 Feb 2025.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [A Full Text-Dependent End to End Mispronunciation Detection and Diagnosis with Easy Data Augmentation Techniques](https://mlsanity.com/paper/2104.08428/) (2021)
- [Acoustic data-driven lexicon learning based on a greedy pronunciation selection framework](https://mlsanity.com/paper/1706.03747/) (2017)
- [Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognition](https://mlsanity.com/paper/2306.02534/) (2023)

## Built on by (most cited first, 2 of 2)

- [Investigating Transcription Normalization in the Faetar ASR Benchmark](https://mlsanity.com/paper/2508.11771/) (2025)
- [Revisiting Rule-Based Stuttering Detection: A Comprehensive Analysis of Interpretable Models for Clinical Applications](https://mlsanity.com/paper/2508.16681/) (2025)

## Abstract (arXiv)

Recent advancements in machine learning have significantly improved speech recognition, but recognizing speech from non-fluent or accented speakers remains a challenge. Previous efforts, relying on rule-based pronunciation patterns, have struggled to fully capture non-native errors. We propose two data-driven approaches using speech corpora to automatically detect mispronunciation patterns. By aligning non-native phones with their native counterparts using attention maps, we achieved a 5.7% improvement in speech recognition on native English datasets and a 12.8% improvement for non-native English speakers, particularly Korean speakers. Our method offers practical advancements for robust Automatic Speech Recognition (ASR) systems particularly for situations where prior linguistic knowledge is not applicable.