Interactive Learning of Teacher-student Model for Short Utterance Spoken Language Identification
Peng Shen, Xugang Lu, Sheng Li, Hisashi Kawai · 2019
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-student methods use fixed pre-trained teacher models, that makes it difficult to optimize student models. In this paper, rather than using a fixed pre-trained teacher model, we investigate an interactive teacher-student learning by adjusting the teacher model with reference to the performance of the student model when the student model is stuck in a local minimum. Experiments on a 10-language LID task were carried out to test the algorithm. Our results showed its effectiveness of the proposed algorithm on short utterance LID tasks.