T-vectors: Weakly Supervised Speaker Identification Using Hierarchical Transformer Model
This addresses speaker identification in multi-speaker audio for applications like transcription or security, but it is incremental as it builds on existing transformer and memory techniques.
The paper tackled the problem of identifying multiple speakers in recordings without knowing their locations by proposing a hierarchical transformer model with a memory mechanism, achieving relative improvements of 13.3% and 10.5% over strong baselines in different data scenarios.
Identifying multiple speakers without knowing where a speaker's voice is in a recording is a challenging task. This paper proposes a hierarchical network with transformer encoders and memory mechanism to address this problem. The proposed model contains a frame-level encoder and segment-level encoder, both of them make use of the transformer encoder block. The multi-head attention mechanism in the transformer structure could better capture different speaker properties when the input utterance contains multiple speakers. The memory mechanism used in the frame-level encoders can build a recurrent connection that better capture long-term speaker features. The experiments are conducted on artificial datasets based on the Switchboard Cellular part1 (SWBC) and Voxceleb1 datasets. In different data construction scenarios (Concat and Overlap), the proposed model shows better performance comparaing with four strong baselines, reaching 13.3% and 10.5% relative improvement compared with H-vectors and S-vectors. The use of memory mechanism could reach 10.6% and 7.7% relative improvement compared with not using memory mechanism.