SIMULATED-DATA ADAPTATION BASED PIECEWISE LINEAR TRANSFORMATION FOR ROBUST SPEECH RECOGNITION

Authors

Thatphithakkul Nattanun
Kruatrachue Boontee
Wutiwiwatchai Chai
Marukatat Sanparith
Boonpiam Vataya

Abstract

This paper proposes an efficient method of simulated-data adaptation for robust speech recognition. The method is applied to tree-structured piecewise linear transformation (PLT). The original PLT selects an acoustic model using tree-structured HMMs and the acoustic model is adapted by input speech in an unsupervised scheme. This adaptation can degrade the acoustic model if the input speech is incorrectly transcribed during the adaptation process. Moreover, adaptation may not be effective if only the input speech is used. Our proposed method increases the size of adaptation data by adding noise portions from the input speech to a set of prerecorded clean speech, of which correct transcriptions are known. We investigate various configurations of the proposed method. Evaluations are performed with both additive and real noisy speech. The experimental results show that the proposed system reaches higher recognition rate than MLLR, HMM-based model selection and PLT.

Publication Date

11-16-2017

Recommended Citation

Nattanun, Thatphithakkul; Boontee, Kruatrachue; Chai, Wutiwiwatchai; Sanparith, Marukatat; and Vataya, Boonpiam (2017) "SIMULATED-DATA ADAPTATION BASED PIECEWISE LINEAR TRANSFORMATION FOR ROBUST SPEECH RECOGNITION," ASEAN Journal on Science and Technology for Development: Vol. 24: No. 4, Article 1.
DOI: https://doi.org/10.29037/ajstd.209
Available at: https://ajstd.ubd.edu.bn/journal/vol24/iss4/1