Li Wan

Generalized End-to-End Loss for Speaker Verification

2017 · arXiv (Cornell University)

In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the …

Add a topic feature to learning to rank model

2016

Latent Dirichlet Allocation (LDA), is heavily cited in the machine learning literature, but its feasibility and effectiveness in information retrieval is mostly unknown. Learning to rank is useful for document retrieval, it uses feature vector …