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Found 4 papers, 0 papers with code
EM-Network: Oracle Guided Self-distillation for Sequence Learning
We introduce EM-Network, a novel self-distillation approach that effectively leverages target information for supervised sequence-to-sequence (seq2seq) learning.
Inter-KD: Intermediate Knowledge Distillation for CTC-Based Automatic Speech Recognition
Recently, the advance in deep learning has brought a considerable improvement in the end-to-end speech recognition field, simplifying the traditional pipeline while producing promising results.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus
The experimental results verify the effectiveness of the proposed method in terms of naturalness, intelligibility, and speaker generalization.
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models
Extending this supervised scheme further, we introduce a new type of teacher model for connectionist temporal classification (CTC)-based sequence models, namely Oracle Teacher, that leverages both the source inputs and the output labels as the teacher model's input.
