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LSTM deep neural networks postfiltering for improving the quality of synthetic voices
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
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. ...
Hybrid speech enhancement with wiener filters and deep LSTM denoising autoencoders
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 ...
Hidden Markov Models for artificial voice production and accent modification
In this paper, we consider the problem of accent modification between Castilian Spanish and Mexican Spanish. This is an interesting application area for tasks such as the automatic dubbing of pictures and videos with ...