Overlapped Speech Detection in Multi-Party Meetings

Detection of simultaneous speech in meeting recordings is a difficult problem due both to the complexity of the meeting itself and the environment surrounding it. The system proposes the use of gammatone-like spectrogram-based linear predictor coefficients on distant microphone channel data for overlap detection functions. The framework utilized the Augmented Multiparty Interaction (AMI) conference corpus to assess model performance. The proposed system offers enhancements over base line feature set models for classification

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.Comment: Accepted to Interspeech 202