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Found 5 papers, 0 papers with code
Adversarial Speaker-Consistency Learning Using Untranscribed Speech Data for Zero-Shot Multi-Speaker Text-to-Speech
Several recently proposed text-to-speech (TTS) models achieved to generate the speech samples with the human-level quality in the single-speaker and multi-speaker TTS scenarios with a set of pre-defined speakers.
Fully Unsupervised Training of Few-shot Keyword Spotting
For training a few-shot keyword spotting (FS-KWS) model, a large labeled dataset containing massive target keywords has known to be essential to generalize to arbitrary target keywords with only a few enrollment samples.
Disentangled Speaker Representation Learning via Mutual Information Minimization
The experimental results show that fine-tuning with a disentanglement framework on a existing pre-trained model is valid and can further improve performance.
Transfer Learning Framework for Low-Resource Text-to-Speech using a Large-Scale Unlabeled Speech Corpus
The experimental results verify the effectiveness of the proposed method in terms of naturalness, intelligibility, and speaker generalization.
Expressive Text-to-Speech using Style Tag
In this work, we propose StyleTagging-TTS (ST-TTS), a novel expressive TTS model that utilizes a style tag written in natural language.
