Feature adaptation for robust mobile speech recognition

Hyeopwoo Lee, Dongsuk Yook

Research output: Contribution to journalArticlepeer-review


Feature adaptation such as feature space maximum likelihood linear regression (FMLLR) is useful for robust mobile speech recognition. However, as the amount of adaptation data increases, feature adaptation performance becomes saturated quickly due to its limitation of global transformation. To handle this problem, we propose regression tree based FMLLR which can adopt multiple transformations as the amount of adaptation data increases. An experimental result shows that the proposed method reduces the recognition error by 11.8% further for speaker adaptation task and by 13.6% further for noisy environment adaptation task compared to the conventional method.

Original languageEnglish
Article number6415011
Pages (from-to)1393-1398
Number of pages6
JournalIEEE Transactions on Consumer Electronics
Issue number4
Publication statusPublished - 2012

Bibliographical note

Funding Information:
1This work was supported by the Korea Research Foundation (KRF) grant funded by the Korea government (MEST) (No. 2011-0002906).


  • Speech recognition
  • environment adaptation
  • feature adaptation
  • feature spacemaximum likelihood linear regression (FMLLR)
  • regression tree
  • speaker adaptation

ASJC Scopus subject areas

  • Media Technology
  • Electrical and Electronic Engineering


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