4 accepted papers
Due to the modality discrepancy between textual and acoustic modeling, efficiently transferring linguistic knowledge from a pretrained language model (PLM) to acoustic encoding for automatic speech recognition (ASR) still remains a challenging task. In this study, we propose a cross-modality knowled…
Due to the mismatch of statistical distributions of acoustic speech between training and testing sets, the performance of spoken language identification (SLID) could be drastically degraded. In this paper, we propose an unsupervised neural adaptation model to deal with the distribution mismatch prob…
Short utterance-based spoken language identification (LID) is a challenging task due to the large variation of its feature representation. Improving feature representation of short utterances using a teacher-student method has been shown its effectiveness for LID tasks. However, conventional teacher…
I-vector is a state-of-the-art technique widely used in spoken language identification systems. Since i-vectors include total variability factors, discriminant analysis methods have been introduced to find the most discriminative features while removing the undesired variables for language identific…