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ONDEL YANG, L. LAM-YEE-MUI, L. KOCOUR, M. CORRO, C. BURGET, L.
Original Title
GPU-Accelerated Forward-Backward Algorithm with Application to Lattice-Free MMI
Type
conference paper
Language
English
Original Abstract
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 PyChaina 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".
Keywords
Lattice-Free MMI, end-to-end ASR, Julia language, forward-backward
Authors
ONDEL YANG, L.; LAM-YEE-MUI, L.; KOCOUR, M.; CORRO, C.; BURGET, L.
Released
27. 5. 2022
Publisher
IEEE Signal Processing Society
Location
Singapore
ISBN
978-1-6654-0540-9
Book
ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Pages from
8417
Pages to
8421
Pages count
5
URL
https://ieeexplore.ieee.org/document/9746824
BibTex
@inproceedings{BUT178409, author="Lucas Antoine Francois {Ondel} and L'ea-Marie {Lam-Yee-Mui} and Martin {Kocour} and Caio Filippo {Corro} and Lukáš {Burget}", title="GPU-Accelerated Forward-Backward Algorithm with Application to Lattice-Free MMI", booktitle="ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings", year="2022", pages="8417--8421", publisher="IEEE Signal Processing Society", address="Singapore", doi="10.1109/ICASSP43922.2022.9746824", isbn="978-1-6654-0540-9", url="https://ieeexplore.ieee.org/document/9746824" }