# Investigating the role of L1 in automatic pronunciation evaluation of L2 speech

arXiv: https://arxiv.org/abs/1807.01738 · HTML: https://mlsanity.com/paper/1807.01738/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2018.

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

- [Transformer-Based Multi-Aspect Multi-Granularity Non-Native English Speaker Pronunciation Assessment](https://mlsanity.com/paper/2205.03432/) (2022)
- [PRiSM: Benchmarking Phone Realization in Speech Models](https://mlsanity.com/paper/2601.14046/) (2026)
- [Topics in the Study of the Pragmatic Functions of Phonetic Reduction in Dialog](https://mlsanity.com/paper/2405.01376/) (2024)
- [Proficiency-Aware Adaptation and Data Augmentation for Robust L2 ASR](https://mlsanity.com/paper/2510.10738/) (2025)
- [Multi-granularity Interactive Attention Framework for Residual Hierarchical Pronunciation Assessment](https://mlsanity.com/paper/2601.01745/) (2026)

## Abstract (arXiv)

Automatic pronunciation evaluation plays an important role in pronunciation training and second language education. This field draws heavily on concepts from automatic speech recognition (ASR) to quantify how close the pronunciation of non-native speech is to native-like pronunciation. However, it is known that the formation of accent is related to pronunciation patterns of both the target language (L2) and the speaker's first language (L1). In this paper, we propose to use two native speech acoustic models, one trained on L2 speech and the other trained on L1 speech. We develop two sets of measurements that can be extracted from two acoustic models given accented speech. A new utterance-level feature extraction scheme is used to convert these measurements into a fixed-dimension vector which is used as an input to a statistical model to predict the accentedness of a speaker. On a data set consisting of speakers from 4 different L1 backgrounds, we show that the proposed system yields improved correlation with human evaluators compared to systems only using the L2 acoustic model.