Feature combination using linear discriminant analysis and its pitfalls

Abstract

In this paper, Linear Discriminant Analysis (LDA) is investigated with respect to the combination of different acoustic features for automatic speech recognition. It is shown that the combination of acoustic features using LDA does not consistently lead to improvements in word error rate. A detailed analysis of the recognition results on the Verbmobil (VM II) and on the English portion of the European Parliament Plenary Sessions (EPPS) corpus is given. This includes an independent analysis of the effect of the dimension of the input to LDA, the effect of strongly correlated input features, as well as a detailed numerical analysis of the generalized eigenvalue problem underlying LDA. Relative improvements in word error rate of up to 5% were observed for LDA-based combination of multiple acoustic features.