31 research outputs found
LSTM Deep Neural Networks Postfiltering for Improving the Quality of Synthetic Voices
Recent developments in speech synthesis have produced systems capable of
outcome intelligible speech, but now researchers strive to create models that
more accurately mimic human voices. One such development is the incorporation
of multiple linguistic styles in various languages and accents.
HMM-based Speech Synthesis is of great interest to many researchers, due to
its ability to produce sophisticated features with small footprint. Despite
such progress, its quality has not yet reached the level of the predominant
unit-selection approaches that choose and concatenate recordings of real
speech. Recent efforts have been made in the direction of improving these
systems.
In this paper we present the application of Long-Short Term Memory Deep
Neural Networks as a Postfiltering step of HMM-based speech synthesis, in order
to obtain closer spectral characteristics to those of natural speech. The
results show how HMM-voices could be improved using this approach.Comment: 5 pages, 5 figure
Applying Deep Bidirectional LSTM and Mixture Density Network for Basketball Trajectory Prediction
Data analytics helps basketball teams to create tactics. However, manual data
collection and analytics are costly and ineffective. Therefore, we applied a
deep bidirectional long short-term memory (BLSTM) and mixture density network
(MDN) approach. This model is not only capable of predicting a basketball
trajectory based on real data, but it also can generate new trajectory samples.
It is an excellent application to help coaches and players decide when and
where to shoot. Its structure is particularly suitable for dealing with time
series problems. BLSTM receives forward and backward information at the same
time, while stacking multiple BLSTMs further increases the learning ability of
the model. Combined with BLSTMs, MDN is used to generate a multi-modal
distribution of outputs. Thus, the proposed model can, in principle, represent
arbitrary conditional probability distributions of output variables. We tested
our model with two experiments on three-pointer datasets from NBA SportVu data.
In the hit-or-miss classification experiment, the proposed model outperformed
other models in terms of the convergence speed and accuracy. In the trajectory
generation experiment, eight model-generated trajectories at a given time
closely matched real trajectories