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Assessing the robustness of recurrent neural networks to enhance the spectrum of reverberated speech
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 ...
Reconstructing fundamental frequency from noisy speech using initialized autoencoders
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 ...
A performance evaluation of several artificial neural networks for mapping speech spectrum parameters
In this work, we compare different neural network architectures, for the task of mapping spectral coefficients of noisy speech signals with those corresponding to natural speech. In previous works on the subject, fully-connected ...