End-to-End Multi-Speaker Speech Recognition using Speaker Embeddings and Transfer Learning

被引：12
|
作者：
Denisov, Pavel [1 ]
Ngoc Thang Vu [1 ]
机构：
[1] Univ Stuttgart, Inst Nat Language Proc IMS, Stuttgart, Germany
来源：
INTERSPEECH 2019 | 2019年
关键词：
end-to-end asr; overlapped speech;
D O I：
10.21437/Interspeech.2019-1130
中图分类号：
R36 [病理学]; R76 [耳鼻咽喉科学];
学科分类号：
100104 ; 100213 ;
摘要：
This paper presents our latest investigation on end-to-end automatic speech recognition (ASR) for overlapped speech. We propose to train an end-to-end system conditioned on speaker embeddings and further improved by transfer learning from clean speech. This proposed framework does not require any parallel non-overlapped speech materials and is independent of the number of speakers. Our experimental results on overlapped speech datasets show that joint conditioning on speaker embeddings and transfer learning significantly improves the ASR performance.
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页码：4425 / 4429
页数：5
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