Voice-activity and overlapped speech detection using x-vectors

Abstract

The x-vectors are features extracted from speech signals using pretrained deep neural networks, such that they discriminate well among different speakers. Their main application lies in speaker identification and verification. This manuscript studies, which other properties are encoded in x-vectors. The focus lies on distinguishing between speech signals/noise and utterances of a single speaker versus overlapped-speech. We attempt to show that the x-vector network is capable to extract multi-purpose features, which can be used by several simple back-end classifiers. This means a common feature extracting front-end for the tasks of voice-activity/overlapped speech detection and speaker identification. Compared to the alternative strategy, that is training of independent classifiers including feature extracting layers for each of the tasks, the common front-end saves computational time during both training and test phase.