Target Speaker Voice Activity Detection: a Novel Approach for Multi Speaker Diarization in a Dinner Party Scenario
Speaker diarization for real life scenarios is an extremely challenging problem. Widely used clustering based diarization approaches perform rather poorly in such conditions, mainly due to the limited ability to handle overlapping speech. We propose a novel Target Speaker Voice Activity Detection (TS VAD) approach, which directly predicts an activity of each speaker on each time frame. TS VAD model takes conventional speech features (e.g. MFCC) along with i vectors for each speaker as inputs. A set of binary classification output layers produces activities of each speaker. I vectors can be estimated iteratively, starting with a strong clustering based diarization. We also extend the TS VAD approach to the multi microphone case using a simple attention mechanism on top of hidden representations extracted from the single channel TS VAD model. Moreover, post processing strategies for the predicted speaker activity probabilities are investigated. Experiments on the CHiME 6 unsegmented data show that TS VAD achieves state of the art results outperforming the baseline x vector based system by more than 30% Diarization Error Rate (DER) abs.
INTERSPEECH