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On granularity of prosodic representations in expressive text-to-speech
In expressive speech synthesis it is widely adopted to use latent prosody representations to deal with variability of the data during training.
GaLeNet: Multimodal Learning for Disaster Prediction, Management and Relief
In this work, we propose a multimodal framework (GaLeNet) for assessing the severity of damage by complementing pre-disaster images with weather data and the trajectory of the hurricane.
Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech
In this paper, we present a method for building highly expressive TTS voices with as little as 15 minutes of speech data from the target speaker.
Low-resource expressive text-to-speech using data augmentation
While recent neural text-to-speech (TTS) systems perform remarkably well, they typically require a substantial amount of recordings from the target speaker reading in the desired speaking style.
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