Speaker Naming in Arabic TV Programs

Abstract

Automatic speaker identification is the problem of identifying speakers by their real identities. Previous approaches use textual information as a source of naming, try to associate names to neighbouring speaker segments using linguistic rules. However, these approaches have a few limitations that hinder their application on spoken text. Deep learning approaches for natural language processing have recently reached state-of-the-art results. However, deep learning requires a lot of annotated data which is difficult to obtain in the case of speaker identification task. In this paper, we present two contributions towards integrating deep learning for identifying speakers in news broadcasts: first we realise a dataset in which the names of mentioned speakers are related to the previous, next, current or other speaker turns. Moreover, we present our approach to solve the problem of speaker identification using information obtained from the transcription. We use a Long-term Recurrent Convolutional Network for name assignment and integer linear programming for name propagation into the different segments. We evaluate our model on both assignment and propagation tasks on the test part of the Arabic multi-genre broadcast dataset which consists of 17 TV programs from Aljazeera. The performance is analysed using the evaluation metrics, such as Estimated Global Error Rate (EGER) and Diarization Error Rate (DER). The outcome of the proposed method ensures better performance by achieving the lower EGER of 32.3% and DER of 8.3%.