| Abstract |
Text-to-speech (TTS) synthesis is the automatic conversion of written text to spoken
language. TTS systems play an important role in natural human-computer interaction.
Concatenative speech synthesis and statistical parametric speech synthesis were the
prominent methods used for decades. In the era of Deep learning, end-to-end TTS
systems have dramatically improved the quality of synthetic speech. The aim of this work
was the implementation of an end-to-end neural based TTS system for the Greek
Language. The neural network architecture of Tacotron-2 is used for speech synthesis
directly from text. The system is composed of a recurrent sequence-to-sequence feature
prediction network that maps character embeddings to acoustic features, followed by a
modified WaveNet model acting as a vocoder to synthesize time-domain waveforms from
the predicted acoustic features. Developing TTS systems for any given language is a
significant challenge and requires large amount of high quality acoustic recordings.
Because of this, these systems are only available for the most commonly and widely
spoken languages. In this work, experiments are described for various languages and
databases which are freely available. A Greek database, initially created for speech
recognition, has been obtained from ILSP (Institute for Language and Speech Processing).
In our first experiment, only 3 hours of recorded speech in Greek have been used. Then
the technique of language adaptation has been applied, using 3 hours in Greek and 18
hours in Spanish. We also have applied speaker adaptation in order to produce speech
with specific speakers from our database. Our TTS system for Greek can generate good
quality of speech with very natural prosody. An evaluation with a listening test by 30
volunteers gave a score in MOS (Mean Opinion Score) of 3.15 to our model and 3.82 to
the original recordings.
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End-to-end neural based Greek text-to-speech synthesis
