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Auto-Associative Initialization of LSTM Neural Networks for Fundamental Frequency Detection in Noisy Speech Signals
(2018)
In this paper, we present a new approach for fundamental frequency detection in noisy speech, based on Long Short-term Memory Neural Networks (LSTM). Fundamental frequency is one of the most important parameters of human ...
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 ...
Hidden Markov Models for artificial voice production and accent modification
(2016)
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 ...
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 experimental study on fundamental frequency detection in reverberated speech with pre-trained recurrent neural networks
(2020)
The detection of the fundamental frequency (f0) in speech signals is relevant in areas such as automatic speech recognition and identification, with multiple potential applications. For example, in virtual assistants, ...
Experimental study on transfer learning in denoising autoencoders for speech enhancement
(2020)
The quality of speech signals is affected by a combination of background noise, reverberation, and other distortions in real-life environments. The processing of such signals presents important challenges for tasks such ...
Evaluation of denoising algorithms for footsteps sound classification in noisy environments
(2021)
Identifying a person using footsteps sounds is part
of the recent research in developing biometrics, systems designed
to identify an individual in a group using body measurements.
The sound of footsteps has a short ...
A performance evaluation of several artificial neural networks for mapping speech spectrum parameters
(2020)
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 ...
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 ...
Measuring the effect of reverberation on statistical parametric speech synthesis
(2020)
Text-to-speech (TTS) synthesis is the technique of generating intelligible speech from a given text. The most recent techniques for TTS are based on machine learning, implementing systems which learn linguistic specifications ...