Learning to rank for automatic speech synthesis – In this work we focus on a new application of human speech detection based on the use of machine learning (ML) techniques to create the speech signal in an artificial world. A machine learning based speech recognition task is used to assess the quality of a speech signal, which can then be used to infer the semantics of the speech signals. Machine learning has recently achieved the rapid development of several speech recognition applications. With a large number of applications such as the speech recognizer, the ML task has achieved great success in its own right. In this paper we study our approach in two different ways: 1) we propose a novel algorithm which can extract the syntactic information from the human speech signal, but has a very limited computational time; 2) we propose a new speech recognition method which can learn the linguistic knowledge from the semantic analysis of a sequence of speech signals. Experiments demonstrate that the new algorithm achieves state-of-the-art performance on English.

In this paper, we propose the solution to the problem of learning a Bayesian posterior using a low-dimensional Euclidean space in particular. We have proposed a novel general framework based on the notion of a low-dimensional Euclidean space. The idea is to map the space into a low-dimensional space using a finite-dimensional Euclidean embedding on the space. Our new formulation in this framework yields a convex relaxation of the posterior probability distribution as a low-dimensional unit and a vector embedding that encodes the posterior probability distribution. The result of the method is that, in a non-convex setting, an unknown variable of interest is given to the posterior probability distribution and the posterior likelihood of the embedding is obtained with the minimax relaxation. We also propose a novel way to learn the embedding using an orthogonal dictionary learning algorithm. Experiments on both synthetic and real data show that the embedding can achieve state-of-the-art performance and outperforms Euclidean-based posterior estimation.

Deep Multi-Objective Goal Modeling

Feature Matching to Improve Large-Scale Image Recognition

Learning to rank for automatic speech synthesis

Comparing Deep Neural Networks to Matching Networks for Age Estimation

Fast, Accurate Metric LearningIn this paper, we propose the solution to the problem of learning a Bayesian posterior using a low-dimensional Euclidean space in particular. We have proposed a novel general framework based on the notion of a low-dimensional Euclidean space. The idea is to map the space into a low-dimensional space using a finite-dimensional Euclidean embedding on the space. Our new formulation in this framework yields a convex relaxation of the posterior probability distribution as a low-dimensional unit and a vector embedding that encodes the posterior probability distribution. The result of the method is that, in a non-convex setting, an unknown variable of interest is given to the posterior probability distribution and the posterior likelihood of the embedding is obtained with the minimax relaxation. We also propose a novel way to learn the embedding using an orthogonal dictionary learning algorithm. Experiments on both synthetic and real data show that the embedding can achieve state-of-the-art performance and outperforms Euclidean-based posterior estimation.