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Improving post-filtering of artificial speech using pre-trained LSTM neural networks
(2019)
Several researchers have contemplated deep learning-based post-filters to increase the quality of statistical parametric speech synthesis, which perform a mapping of the synthetic speech to the natural speech, considering ...
Discriminative multi-stream postfilters based on deep learning for enhancing statistical parametric speech synthesis
(2021)
Statistical parametric speech synthesis based on Hidden Markov Models has been an
important technique for the production of artificial voices, due to its ability to produce results with high intelligibility and sophisticated ...
LSTM deep neural networks postfiltering for improving the quality of synthetic voices
(2016)
Recent developments in speech synthesis have produced systems capable of providing intelligible speech, and researchers now strive to create models that more accurately mimic human voices. One such development is the ...
Improving automatic speech recognition containing additive noise using deep denoising autoencoders of lstm networks
(2016)
Automatic speech recognition systems (ASR) suffer from performance degradation under noisy conditions. Recent work, using deep neural networks to denoise spectral input features for robust ASR, have proved to be successful. ...
LSTM deep neural networks postfiltering for enhancing synthetic voices
(2018)
Recent developments in speech synthesis have produced systems capable of producing speech which closely resembles natural speech, and researchers now strive to create models that more accurately mimic human voices. One ...
Speech synthesis based on Hidden Markov Models and deep learning
(2016)
Speech synthesis based on Hidden Markov Models (HMM)
and other statistical parametric techniques have been a hot topic for
some time. Using this techniques, speech synthesizers are able to produce
intelligible and ...
Reconstructing fundamental frequency from noisy speech using initialized autoencoders
(2020-10)
In this paper, we present a new approach for fundamental frequency (f0) detection in noisy speech, based on Long Short-term Memory Neural Networks (LSTM). f0 is one of the most important parameters of human speech. Its ...
Robustness of LSTM neural networks for the enhancement of spectral parameters in noisy speech signals
(2019)
In this paper, we carry out a comparative performance analysis of Long Short-term Memory (LSTM) Neural Networks for the task of noise reduction. Recent work in this area has shown the advantages of this kind of network for ...
Assessing the robustness of recurrent neural networks to enhance the spectrum of reverberated speech
(2020)
Implementing voice recognition systems and voice analysis in real-life contexts present important challenges, especially when signal recording/registering conditions are adverse. One of the conditions that produce signal ...
An Improved Deep Learning Model for Electricity Price Forecasting
Accurate electricity price forecasting (EPF) is important for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity market. Besides that, EPF becomes critically ...