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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 ...
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. ...
Pre-training Long Short-term Memory neural networks for efficient regression in artificial speech postfiltering
(2018)
Several attempts to enhance statistical parametric speech synthesis have contemplated deep-learning-based postfilters, which learn to perform a mapping of the synthetic speech parameters to the natural ones, reducing the ...
Hybrid speech enhancement with wiener filters and deep LSTM denoising autoencoders
(2018)
Over the past several decades, numerous speech enhancement techniques have been proposed to improve the performance of modern communication devices in noisy environments. Among them, there is a large range of classical ...
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 ...
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 ...
Ensemble of temporal convolutional and long short-term memory neural networks apply to forecasting USDCOP exchange rate
(Universidad EAFITMaestría en Ciencias de los Datos y AnalíticaEscuela de AdministraciónMedellín, 2021)
This paper applies a neural network with ensemble of temporal convolutional network (TCN) and long short-term memory (LSTM) layers approach to forecast foreign exchange rates between the US dollar (USD) and Colombian Peso ...
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 ...
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 ...