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Found 7 papers, 2 papers with code
Deep Speaker Vector Normalization with Maximum Gaussianality Training
In this paper, we argue that this problem is largely attributed to the maximum-likelihood (ML) training criterion of the DNF model, which aims to maximize the likelihood of the observations but not necessarily improve the Gaussianality of the latent codes.
Deep Normalization for Speaker Vectors
Deep speaker embedding has demonstrated state-of-the-art performance in speaker recognition tasks.
Memory-augmented Neural Machine Translation
Neural machine translation (NMT) has achieved notable success in recent times, however it is also widely recognized that this approach has limitations with handling infrequent words and word pairs.
Flexible and Creative Chinese Poetry Generation Using Neural Memory
It has been shown that Chinese poems can be successfully generated by sequence-to-sequence neural models, particularly with the attention mechanism.
Phonetic Temporal Neural Model for Language Identification
Deep neural models, particularly the LSTM-RNN model, have shown great potential for language identification (LID).
Collaborative Learning for Language and Speaker Recognition
This paper presents a unified model to perform language and speaker recognition simultaneously and altogether.
Probabilistic Belief Embedding for Knowledge Base Completion
This paper contributes a novel embedding model which measures the probability of each belief $\langle h, r, t, m\rangle$ in a large-scale knowledge repository via simultaneously learning distributed representations for entities ($h$ and $t$), relations ($r$), and the words in relation mentions ($m$).
