9 research outputs found
Expediting TTS Synthesis with Adversarial Vocoding
Recent approaches in text-to-speech (TTS) synthesis employ neural network
strategies to vocode perceptually-informed spectrogram representations directly
into listenable waveforms. Such vocoding procedures create a computational
bottleneck in modern TTS pipelines. We propose an alternative approach which
utilizes generative adversarial networks (GANs) to learn mappings from
perceptually-informed spectrograms to simple magnitude spectrograms which can
be heuristically vocoded. Through a user study, we show that our approach
significantly outperforms na\"ive vocoding strategies while being hundreds of
times faster than neural network vocoders used in state-of-the-art TTS systems.
We also show that our method can be used to achieve state-of-the-art results in
unsupervised synthesis of individual words of speech.Comment: Published as a conference paper at INTERSPEECH 201