Result Details

Implementing contextual biasing in GPU decoder for online ASR

NIGMATULINA, I.; MADIKERI, S.; VILLATORO-TELLO, E.; MOTLÍČEK, P.; ZULUAGA-GOMEZ, J.; PANDIA, K.; GANAPATHIRAJU, A. Implementing contextual biasing in GPU decoder for online ASR. In Proceedings of the Annual Conference of International Speech Communication Association, INTERSPEECH. Proceedings of Interspeech. Dublin: International Speech Communication Association, 2023. no. 8, p. 4494-4498. ISSN: 1990-9772.
Type
conference paper
Language
English
Authors
NIGMATULINA, I.
Madikeri Srikanth, FIT (FIT)
VILLATORO-TELLO, E.
Motlíček Petr, doc. Ing., Ph.D., DCGM (FIT)
ZULUAGA-GOMEZ, J.
PANDIA, K.
GANAPATHIRAJU, A.
Abstract

GPU decoding significantly accelerates the output of ASR predictions.
While GPUs are already being used for online ASR
decoding, post-processing and rescoring on GPUs have not
been properly investigated yet. Rescoring with available contextual
information can considerably improve ASR predictions.
Previous studies have proven the viability of lattice rescoring
in decoding and biasing language model (LM) weights in offline
and online CPU scenarios. In real-time GPU decoding,
partial recognition hypotheses are produced without lattice generation,
which makes the implementation of biasing more complex.
The paper proposes and describes an approach to integrate
contextual biasing in real-time GPU decoding while exploiting
the standard Kaldi GPU decoder. Besides the biasing of partial
ASR predictions, our approach also permits dynamic context
switching allowing a flexible rescoring per each speech segment
directly on GPU. The code is publicly released1 and tested with
open-sourced test sets.

Keywords

real-time speech recognition, contextual adaptation, GPU decoding, finite-state transducers

URL
Published
2023
Pages
4494–4498
Journal
Proceedings of Interspeech, vol. 2023, no. 8, ISSN 1990-9772
Proceedings
Proceedings of the Annual Conference of International Speech Communication Association, INTERSPEECH
Conference
Interspeech Conference
Publisher
International Speech Communication Association
Place
Dublin
DOI
10.21437/Interspeech.2023-2449
EID Scopus
2-s2.0-85171561485
BibTeX 
@inproceedings{BUT187754,
  author="NIGMATULINA, I. and MADIKERI, S. and VILLATORO-TELLO, E. and MOTLÍČEK, P. and ZULUAGA-GOMEZ, J. and PANDIA, K. and GANAPATHIRAJU, A.",
  title="Implementing contextual biasing in GPU decoder for online ASR",
  booktitle="Proceedings of the Annual Conference of International Speech Communication Association, INTERSPEECH",
  year="2023",
  journal="Proceedings of Interspeech",
  volume="2023",
  number="8",
  pages="4494--4498",
  publisher="International Speech Communication Association",
  address="Dublin",
  doi="10.21437/Interspeech.2023-2449",
  issn="1990-9772",
  url="https://www.isca-archive.org/interspeech_2023/nigmatulina23_interspeech.html"
}
Files
Projects
Real time network, text, and speaker analytics for combating organized crime, EU, Horizon 2020, start: 2019-09-01, end: 2022-12-31, completed
Soudobé metody zpracování, analýzy a zobrazování multimediálních a 3D dat, BUT, Vnitřní projekty VUT, FIT-S-23-8278, start: 2023-03-01, end: 2026-02-28, running
Research groups
Speech Data Mining Research Group BUT Speech@FIT (RG SPEECH)
Departments
Department of Computer Graphics and Multimedia (DCGM)
Back to top