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Found 3 papers, 3 papers with code
Enhancing Speech Intelligibility in Text-To-Speech Synthesis using Speaking Style Conversion
Intelligibility enhancement as quantified by the Intelligibility in Bits (SIIB-Gauss) measure shows that the proposed Lombard-SSDRC TTS system shows significant relative improvement between 110% and 130% in speech-shaped noise (SSN), and 47% to 140% in competing-speaker noise (CSN) against the state-of-the-art TTS approach.
A fully recurrent feature extraction for single channel speech enhancement
Convolutional neural network (CNN) modules are widely being used to build high-end speech enhancement neural models.
A non-causal FFTNet architecture for speech enhancement
By suggesting a shallow network and applying non-causality within certain limits, the suggested FFTNet for speech enhancement (SE-FFTNet) uses much fewer parameters compared to other neural network based approaches for speech enhancement like WaveNet and SEGAN.
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