Speech Enhancement for Multimodal Speaker Diarization System

Abstract

Speaker diarization system identifies the speaker homogenous regions in those set of recordings where multiple speakers are present. It answers the question `who spoke when?'. The data set for speaker diarization usually consists of telephone, meetings, TV/ talk shows, broadcast news and other multi-speaker recordings. In this paper, we present the performance of our proposed multimodal speaker diarization system under noisy conditions. Two types of noises comprising additive white Gaussian noise (AWGN) and realistic environmental noise is used to evaluate the system. To mitigate the effect of noise, we propose to add an LSTM based speech enhancement block in our diarization pipeline. This block is trained on synthesized data set with more than 100 noise types to enhance the noisy speech. The enhanced speech is further used in multimodal speaker diarization system which utilizes a pre-trained audio-visual synchronization model to find the active speaker. High confidence active speaker segments are then used to train the speaker specific clusters on the enhanced speech. A subset of AMI corpus consisting of 5.4 h of recordings is used in this analysis. For AWGN, the LSTM model performance improvement is comparable with Wiener filter while in case of realistic environmental noise, the LSTM model improves significantly as compared to Wiener filter in terms of diarization error rate (DER).