Abstract

Variability in speech pronunciation is widely observed across different linguistic backgrounds, which impacts modern automatic speech recognition performance. Here, we evaluate the performance of a self-supervised speech model in phoneme recognition using direct articulatory evidence. Findings indicate significant differences in phoneme recognition, especially in front vowels, between American English and Indian English speakers. To gain a deeper understanding of these differences, we conduct real-time MRI-based articulatory analysis, revealing distinct velar region patterns during the production of specific front vowels. This underscores the need to deepen the scientific understanding of self-supervised speech model variances to advance robust and inclusive speech technology.

Date

2024

Authors

Xuan Shi, Tiantian Feng, Kevin Huang, Sudarsana Reddy Kadiri, Jihwan Lee, Yijing Lu, Yubin Zhang, Louis Goldstein, Shrikanth Narayanan

Journal

JASA Express Letters

Volume

4

Issue

11

Publisher

AIP Publishing