T-vectors: Weakly Supervised Speaker Identification Using Hierarchical Transformer Model
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Speaker IdentificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Weakly Supervised Training of Speaker Identification Models
We propose an approach for training speaker identification models in a weakly supervised manner. We concentrate on the setting where the training data consists of a set of audio recordings and the speaker annotation is p…
speaker-diarizationSpeaker DiarizationSpeaker IdentificationWeakly Supervised Training of Hierarchical Attention Networks for Speaker Identification
Identifying multiple speakers without knowing where a speaker's voice is in a recording is a challenging task. In this paper, a hierarchical attention network is proposed to solve a weakly labelled speaker identification…
Speaker IdentificationH-VECTORS: Utterance-level Speaker Embedding Using A Hierarchical Attention Model
In this paper, a hierarchical attention network to generate utterance-level embeddings (H-vectors) for speaker identification is proposed. Since different parts of an utterance may have different contributions to speaker…
Speaker IdentificationAdaptive blind audio source extraction supervised by dominant speaker identification using x-vectors
We propose a novel algorithm for adaptive blind audio source extraction. The proposed method is based on independent vector analysis and utilizes the auxiliary function optimization to achieve high convergence speed. The…
Speaker IdentificationOrdered and Binary Speaker Embedding
Modern speaker recognition systems represent utterances by embedding vectors. Conventional embedding vectors are dense and non-structural. In this paper, we propose an ordered binary embedding approach that sorts the dim…
ClusteringRetrievalSpeaker IdentificationSpeaker Recognition