GPU-ACCELERATED FORWARD-BACKWARD ALGORITHM WITH APPLICATION TO LATTICE-FREE MMI

被引：3
|
作者：
Ondel, Lucas [1 ]
Lam-Yee-Mui, Lea-Marie [1 ,3 ]
Kocour, Martin [2 ]
Corro, Caio Filippo [1 ]
Burget, Lukas [2 ]
机构：
[1] Univ Paris Saclay, LISN, CNRS, Orsay, France
[2] Brno Univ Technol, Fac Informat Technol, Brno, Czech Republic
[3] Vocapia Res, Orsay, France
来源：
2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2022年
关键词：
Lattice-Free MMI; end-to-end ASR; Julia language; forward-backward;
D O I：
10.1109/ICASSP43922.2022.9746824
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
We propose to express the forward-backward algorithm in terms of operations between sparse matrices in a specific semiring. This new perspective naturally leads to a GPU-friendly algorithm which is easy to implement in Julia or any programming languages with native support of semiring algebra. We use this new implementation to train a TDNN with the LF-MMI objective function and we compare the training time of our system with PyChain-a recently introduced C++/CUDA implementation of the LF-MMI loss. Our implementation is about two times faster while not having to use any approximation such as the "leaky-HMM".
引用
收藏
页码：8417 / 8421
页数：5
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