Interactive Learning of Teacher-student Model for Short Utterance Spoken Language Identification
By Peng Shen, Xugang Lu, Sheng Li, Hisashi Kawai. 2019
12 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Signal Processing
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Citations in the corpus
- Knowledge Distillation: A Survey (2020, methodology): “In particular, these KD-based speech recognition applications have spoken language identification (Shen et al. 2018, 2019c, 2020), audio classification (Gao et al. 2019; Perez et al. 2020), text-independent speaker recognition (Ng et al. 2018), speech enhancement (Watanabe et al. 2017), acoustic…”
- Exploring wav2vec 2.0 on speaker verification and language identification (2020, background): “Recently, neural networks trained with a large amount of labeled data can meet most industrial needs in the field of speech processing [1, 2, 3, 4, 5, 6].”