Although isolated phoneme classification using features from time-domain phase space reconstruction has been investigated recently, the best representation of feature vectors for the discriminability over phoneme classes is still an open question. This paper applies Principal Component Analysis (PCA) to feature vectors from the reconstructed phase space. By using PCA projection, the basis of the feature space is orthogonalized. A Bayes classifier uses the transformed feature vectors to classify phoneme exemplars. The results show that the classification accuracy with the PCA method surpasses the accuracy using only original features in most cases. PCA projection was implemented in three ways over the reconstructed phase space on both speaker-dependent and speaker-independent data. Models are trained and tested using data drawn from the TIMIT database.
|State||Published - 2003|
|Event||2003 ISCA Tutorial and Research Workshop on Nonlinear Speech Processing, NOLISP 2003 - Le Croisic, France|
Duration: May 20 2003 → May 23 2003
|Conference||2003 ISCA Tutorial and Research Workshop on Nonlinear Speech Processing, NOLISP 2003|
|Period||5/20/03 → 5/23/03|
Bibliographical noteFunding Information:
This paper is based upon work supported by the National Science Foundation under Grant No. IIS-0113508.
© NOLISP 2003. All Rights Reserved.
ASJC Scopus subject areas
- Artificial Intelligence
- Signal Processing
- Linguistics and Language