Improved Class Definition in Two Dimensional Linear Discriminant Analysis of Speech

Two-dimensional linear discriminant analysis (2DLDA) is a popular feature transformation being applied in current automatic speech recognition (ASR). The parameters of 2DLDA are usually computed on labelled training data partitioned into phonetic classes. It is generally known that one phonetic class contains speech data collected from different speakers with different speech variability and context for the same phonetic unit. Therefore, many clusters exist in each phonetic class. The mentioned effects are not taken into account in the conventional 2DLDA. In this paper, we present an efficient improvement of 2DLDA, which involves the well-known K-means clustering technique to modify the standard class definition. The clustering algorithm is used to identify the existing clusters in the basic classes, which are treated as the new classes for the subsequent 2DLDA estimation. The proposed method is thoroughly evaluated in Slovak triphone-based large vocabulary continuous speech recognition (LVCSR) task. The modified 2DLDA is compared to the state-of-the-art Mel-frequency cepstral coefficients (MFCCs) and to conventional LDA. The results show that the modified 2DLDA features outperform the MFCCs, LDA and also lead to improvement over the conventional 2DLDA.