Audio style transfer in non-native speech recognition

Kacper Pawel Radzikowski

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Current automatic speech recognition (ASR) systems achieve the over 90-95% accuracy, depending on methodology applied and datasets. However, the accuracy drops significantly, while the ASR system is being used with a non-native speaker of the language to be recognized, mainly because of specific pronunciation features. At the same time, the volume of labeled datasets of non-native speech samples is extremely limited both in size as well as in the number of existing languages, which makes it difficult to train sufficiently accurate ASR systems targeted for non-native speakers. Therefore applying a different method is necessary. In this paper, we suggest an idea for an alternative approach to the problem, by employing so-called style transfer methodology. Style transfer, used mainly in graphical domain until now, could help solve the problem of non-native speech. Another advantage is that the style transferring algorithm could be compatible with already existing ASR systems, which means it would not be necessary to train new systems which can be difficult and time consuming.

Original languageEnglish
Title of host publicationPhotonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018
EditorsRyszard S. Romaniuk, Maciej Linczuk
PublisherSPIE
ISBN (Electronic)9781510622036
DOIs
Publication statusPublished - 2018 Jan 1
Externally publishedYes
EventPhotonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018 - Wilga, Poland
Duration: 2018 Jun 32018 Jun 10

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10808
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferencePhotonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018
CountryPoland
CityWilga
Period18/6/318/6/10

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Keywords

  • Artificial intelligence
  • Deep learning
  • Machine learning
  • Non-native speaker
  • Speech recognition
  • Style transfer

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Cite this

Radzikowski, K. P. (2018). Audio style transfer in non-native speech recognition. In R. S. Romaniuk, & M. Linczuk (Eds.), Photonics Applications in Astronomy, Communications, Industry, and High-Energy Physics Experiments 2018 [1080839] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 10808). SPIE. https://doi.org/10.1117/12.2501495