Detail publikace

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.

Originální název

Implementing contextual biasing in GPU decoder for online ASR

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

NIGMATULINA, I.; MADIKERI, S.; VILLATORO-TELLO, E.; MOTLÍČEK, P.; ZULUAGA-GOMEZ, J.; PANDIA, K.; GANAPATHIRAJU, A.

Vydáno

20. 8. 2023

Nakladatel

International Speech Communication Association

Místo

Dublin

ISSN

1990-9772

Periodikum

Proceedings of Interspeech

Ročník

2023

Číslo

8

Stát

Francouzská republika

Strany od

4494

Strany do

4498

Strany počet

5

URL

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"
}