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Identifier |
000433804 |
Title |
Modeling of speech signals using recurrent neural networks |
Alternative Title |
Μοντελοποίηση σημάτων φωνής με την χρήση αναδρομικών νευρωνικών |
Author
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Παπαδάκη, Αικατερίνη Ι
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Thesis advisor
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Κομίνης, Ιωάννης
Πανταζής, Γιάννης
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Reviewer
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Μακρής, Κωνσταντίνος
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Abstract |
Speech is the primary means of communication between humans. A speech
signal waveform corresponds to the variation of air pressure over time. Over
two hundred years ago there have been early efforts to produce synthetic
speech, using mechanical apparatus. Recently, ongoing activities have been
focused on the artificial production of human speech, or else speech synthesis.
Particularly, there have been developed vocoders, analyzers and synthesizers of
human voice signals, based on mathematical models. Furthermore, neural
vocoders, vocoders based on artificial neural networks, have introduced a new
area in speech synthesis.
Goal of this study is to build a neural vocoder which combines deep
learning with prior knowledge of the inherent sinusoidal nature of speech, in
contrast with the majority of available neural based vocoders which discard
signal processing in favor of neural networks. Particularly, the origin of the
present study is modeling complex multi-component AM and FM sinusoidal
waves with the property to represent speech signals, employing Recurrent
Neural Networks (RNNs). The target is to develop a light neural vocoder, faster
than WaveRNN, which achieves state-of-the-art performance. In the context of
this thesis, we implement a variant of the WaveRNN model and we present the
generated state-of-the-art results. Furthermore, we examine the proposed model’s
performance using synthetic as well as real speech signals.
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Language |
English |
Subject |
Neural vocoder |
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Recurrent neural networks |
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Sinusoidal signals |
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Αναδρομικά νευρωνικά δύκτια |
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Ημιτονοειδή σήματα |
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Νευρονικός Φωνοκωδικοποιητής |
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Νευρωνικά δύκτια |
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Σήματα φωνής |
Issue date |
2021-03-24 |
Collection
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School/Department--School of Sciences and Engineering--Department of Physics--Post-graduate theses
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Type of Work--Post-graduate theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/7/d/5/metadata-dlib-1604663338-495972-29058.tkl
|
Views |
453 |