H-Vectors: Utterance-Level Speaker Embedding Using A Hierarchical Attention Model

In this paper, a hierarchical attention network is proposed to generate utterance-level embeddings (H-vectors) for speaker identification and verification. Since different parts of an utterance may have different contributions to speaker identities, the use of hierarchical structure aims to learn speaker related information locally and globally. In the proposed approach, frame-level encoder and attention are applied on segments of an input utterance and generate individual segment vectors. Then, segment level attention is applied on the segment vectors to construct an utterance representation. To evaluate the effectiveness of the proposed approach, the data of the NIST SRE2008 Part1 is used for training, and two datasets, the Switchboard Cellular (Part1) and the CallHome American English Speech, are used to evaluate the quality of extracted utterance embeddings on speaker identification and verification tasks. In comparison with two baselines, X-vectors and X-vectors+Attention, the obtained results show that the use of H-vectors can achieve a significantly better performance. Furthermore, the learned utterance-level embeddings are more discriminative than the two baselines when mapped into a 2D space using t-SNE.