Analysis of temporal aggregation and dimensionality reduction on feature sets for speaker identification in wireless acoustic sensor networks

In this paper we analyze the impact of temporal feature aggregation and feature dimensionality reduction on the performance of speaker identification tasks. We investigate these two processing steps in the context of communication layer constraints, such as limited bitrate, and privacy constraints at node level, of a wireless acoustic sensor network. To this end, we extract Modulation-MFCC features and state-of-the-art i-vectors for speaker identification, and investigate temporal aggregation and dimensionality reduction in the feature extraction process. In the evaluation, we use clean data as well as reverberant data to assess the feature sets for different application scenarios. It is found that temporal aggregation has a positive effect on increasing speaker identification performance while respecting the aforementioned privacy constraints and that Linear Discriminant Analysis can be successfully employed for dimensionality reduction.