Fuzzy clustering in HMM-based triphone classes of 2DLDA in Slovak LVCSR

This paper presents a new approach to improve the standard class definition in two-dimensional linear discriminant analysis (2DLDA). It is known that an HMM-based triphone class contains data collected from many speakers with different speech variability. Thus, there exist many clusters in each class, whose composition has an influence to 2DLDA. We propose to employ the fuzzy C-means clustering to identify the clusters in each class and treat them as new classes. This work follows our past research focused on improving 2DLDA by K-means clustering. Concurrently, we compare the previously published results with the ones evaluated in this paper. The proposed approach is evaluated in Slovak HMM-based large vocabulary continuous speech recognition (LVCSR). It is shown that the proposed method outperforms the Mel-frequency cepstral coefficients (MFCCs), conventional LDA, 2DLDA and it achieves competitive results compared to the K-means algorithm evaluated in the same framework.