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D. Filimonov

12 papers hereh-index 11409 citations25 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author8
  • last author1

Across the 11 of 12 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • eess.AS2
  • cs.AI1
same name
  • D. Filimonov — 2 papers, h 6
  • D. Filimonov — 1 paper, h 1
  • D. Filimonov — 1 paper, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192025
most citedLow-rank Adaptation of Large Language Model Rescoring for Parameter-Efficient Speech Recognition

38 citations · 75 across the 8 of their papers we have counts for

collaborators
Showing 2020 · cs.CLShow all

4 papers · 2 filters

cs.CL2020★ 7 cited

Improving accuracy of rare words for RNN-Transducer through unigram shallow fusion

Vijay Ravi, Yile Gu, Ankur Gandhe +5

End-to-end automatic speech recognition (ASR) systems, such as recurrent neural network transducer (RNN-T), have become popular, but rare word remains a challenge. In this paper, w…

cs.CL2020

Multi-task Language Modeling for Improving Speech Recognition of Rare Words

Chao-Han Huck Yang, Linda Liu, Ankur Gandhe +4

End-to-end automatic speech recognition (ASR) systems are increasingly popular due to their relative architectural simplicity and competitive performance. However, even though the…

cs.CL2020

Neural Composition: Learning to Generate from Multiple Models

Denis Filimonov, Ravi Teja Gadde, Ariya Rastrow

Decomposing models into multiple components is critically important in many applications such as language modeling (LM) as it enables adapting individual components separately and…

cs.CL2020★ 12 cited

Neural Machine Translation For Paraphrase Generation

Alex Sokolov, Denis Filimonov

Training a spoken language understanding system, as the one in Alexa, typically requires a large human-annotated corpus of data. Manual annotations are expensive and time consuming…

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