Integration of Phoneme-Subspaces Using ICA for Speech Feature Extraction and Recognition

Abstract

In our previous work, the use of PCA instead of DCT shows robustness in distorted speech recognition because the main speech element is projected onto low-order features, while the noise or distortion element is projected onto high-order features [1]. This paper introduces a new feature extraction technique that collects the correlation information among phoneme subspaces and their elements are statistically mutual independent. The proposed speech feature vector is generated by projecting observed vector onto integrated space obtained by PCA and ICA. The performance evaluation shows that the proposed method provides a higher isolated word recognition accuracy than conventional methods in some reverberant conditions.