标题： 使用发音特征的L2音素用于鲁棒语音识别 显示英文标题

标题： Incorporating L2 Phonemes Using Articulatory Features for Robust Speech Recognition

摘要： 有限的非母语语音数据集的可用性给自动语音识别（ASR）带来了重大挑战，以缩小母语者和非母语者之间的性能差距。 为了解决这个问题，本研究的重点是通过发音特征分析，高效地融入L2音素，其中在本工作中指的是韩语音素。 这不仅能够准确建模发音变体，还允许利用母语韩语和英语语音数据集。 我们以端到端的方式使用无格子最大互信息（LF-MMI）目标，训练声学模型以对齐并预测多个发音候选中的一个。 实验结果表明，所提出的方法仅通过L1语音数据训练就能提高韩语L2语音的ASR准确性。 此外，在L2语音上进行微调可以在不牺牲性能的情况下提高L1和L2语音的识别准确性。 显示英文摘要

摘要： The limited availability of non-native speech datasets presents a major challenge in automatic speech recognition (ASR) to narrow the performance gap between native and non-native speakers. To address this, the focus of this study is on the efficient incorporation of the L2 phonemes, which in this work refer to Korean phonemes, through articulatory feature analysis. This not only enables accurate modeling of pronunciation variants but also allows for the utilization of both native Korean and English speech datasets. We employ the lattice-free maximum mutual information (LF-MMI) objective in an end-to-end manner, to train the acoustic model to align and predict one of multiple pronunciation candidates. Experimental results show that the proposed method improves ASR accuracy for Korean L2 speech by training solely on L1 speech data. Furthermore, fine-tuning on L2 speech improves recognition accuracy for both L1 and L2 speech without performance trade-offs.