Analysis and Selection of Prosodic Features for Language Identification

Abstract

Prosodic features are relatively simple in their structures and are believed to be effective in some speech recognition tasks. However, this kind of features is subject to undesirable bias factors, such as speaking styles. To cope with this, researchers have suggested various normalization and measure methods to the features, which makes the feature inventory very large. In this paper, we use a mutual information criterion to analyze and select a number of prosody-related features in a language identification (LID) task. Among twelve optimal features, eight of them are elaborated in this paper. The feature analysis metric, z-score, is shown to have a moderate to high correlation with LID accuracies. Feature selection proposed in this paper brings about the best performance among all prosodic LID systems to our knowledge. A further attempt in system fusion shows a 13% relative improvement the prosodic LID system brings to the conventional phonotactic approach to LID.