Multi-view CCA-based acoustic features for phonetic recognition across speakers and domains

Abstract

Canonical correlation analysis (CCA) and kernel CCA can be used for unsupervised learning of acoustic features when a second view (e.g., articulatory measurements) is available for some training data, and such projections have been used to improve phonetic frame classification. Here we study the behavior of CCA-based acoustic features on the task of phonetic recognition, and investigate to what extent they are speaker-independent or domain-independent. The acoustic features are learned using data drawn from the University of Wisconsin X-ray Microbeam Database (XRMB). The features are evaluated within and across speakers on XRMB data, as well as on out-of-domain TIMIT and MOCHA-TIMIT data. Experimental results show consistent improvement with the learned acoustic features over baseline MFCCs and PCA projections. In both speaker-dependent and cross-speaker experiments, phonetic error rates are improved by 4-9% absolute (10-23% relative) using CCA-based features over baseline MFCCs. In cross-domain phonetic recognition (training on XRMB and testing on MOCHA or TIMIT), the learned projections provide smaller improvements.