Better acoustic normalization in subject independent acoustic-to-articulatory inversion: Benefit to recognition

Amber Afshan; Prasanta Kumar Ghosh

ICASSP 2016accepted0 citations

Better acoustic normalization in subject independent acoustic-to-articulatory inversion: Benefit to recognition

Abstract

In subject independent acoustic-to-articulatory inversion (SII), the training and test subjects are in general different, whereas subject dependent inversion (SDI) uses the same training and test subjects. Thus, acoustic normalization is used to compensate for the mismatch between the training and the test subjects in SII. We show that a better acoustic normalization not only results in better articulatory estimates using SII, but also improves the broad class phonetic recognition accuracy, when the articulatory features estimated from SII are used for recognition. Recognition experiments using male and female subjects from the MOCHA-TIMIT corpus also show that there is no significant difference between the recognition accuracy using the articulatory features obtained by the best acoustic normalization in SII and that obtained using SDI as well as directly measured articulatory features.

BibTeX

@inproceedings{icassp2016_betteracousticno,
  title = {Better acoustic normalization in subject independent acoustic-to-articulatory inversion: Benefit to recognition},
  author = {Amber Afshan and Prasanta Kumar Ghosh},
  booktitle = {ICASSP 2016},
  year = {2016}
}