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Found 7 papers, 0 papers with code
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis
In this work, we present the SOMOS dataset, the first large-scale mean opinion scores (MOS) dataset consisting of solely neural text-to-speech (TTS) samples.
Self-supervised learning for robust voice cloning
This method enables us to train our model in an unlabeled multispeaker dataset as well as use unseen speaker embeddings to copy a speaker's voice.
Karaoker: Alignment-free singing voice synthesis with speech training data
Existing singing voice synthesis models (SVS) are usually trained on singing data and depend on either error-prone time-alignment and duration features or explicit music score information.
Fine-grained Noise Control for Multispeaker Speech Synthesis
A text-to-speech (TTS) model typically factorizes speech attributes such as content, speaker and prosody into disentangled representations. Recent works aim to additionally model the acoustic conditions explicitly, in order to disentangle the primary speech factors, i. e. linguistic content, prosody and timbre from any residual factors, such as recording conditions and background noise. This paper proposes unsupervised, interpretable and fine-grained noise and prosody modeling.
Generating Multilingual Gender-Ambiguous Text-to-Speech Voices
The gender of any voice user interface is a key element of its perceived identity.
Low-Resource Cross-Domain Singing Voice Synthesis via Reduced Self-Supervised Speech Representations
In this paper, we propose a singing voice synthesis model, Karaoker-SSL, that is trained only on text and speech data as a typical multi-speaker acoustic model.
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification
We thus redefine the emotion labeling problem by shifting it from a traditional classification model to an ordinal classification one, where discrete emotions are arranged in a sequential order according to their valence levels.
