Search Results for author:
Found 5 papers, 1 papers with code
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data
Neural text-to-speech synthesis (NTTS) models have shown significant progress in generating high-quality speech, however they require a large quantity of training data.
Discrete Acoustic Space for an Efficient Sampling in Neural Text-To-Speech
We present a Split Vector Quantized Variational Autoencoder (SVQ-VAE) architecture using a split vector quantizer for NTTS, as an enhancement to the well-known Variational Autoencoder (VAE) and Vector Quantized Variational Autoencoder (VQ-VAE) architectures.
Distribution augmentation for low-resource expressive text-to-speech
This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data.
What does the sea say to the shore? A BERT based DST style approach for speaker to dialogue attribution in novels
We present a complete pipeline to extract characters in a novel and link them to their direct-speech utterances.
Analysis and Utilization of Entrainment on Acoustic and Emotion Features in User-agent Dialogue
Entrainment is the phenomenon by which an interlocutor adapts their speaking style to align with their partner in conversations.
