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Found 6 papers, 1 papers with code
Assessing Phrase Break of ESL Speech with Pre-trained Language Models and Large Language Models
To leverage NLP models, speech input is first force-aligned with texts, and then pre-processed into a token sequence, including words and phrase break information.
Speech BERT Embedding For Improving Prosody in Neural TTS
Experimental results obtained by the Transformer TTS show that the proposed BERT can extract fine-grained, segment-level prosody, which is complementary to utterance-level prosody to improve the final prosody of the TTS speech.
Exploring Machine Speech Chain for Domain Adaptation and Few-Shot Speaker Adaptation
Machine Speech Chain, which integrates both end-to-end (E2E) automatic speech recognition (ASR) and text-to-speech (TTS) into one circle for joint training, has been proven to be effective in data augmentation by leveraging large amounts of unpaired data.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
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Robust Sequence-to-Sequence Acoustic Modeling with Stepwise Monotonic Attention for Neural TTS
In this paper, we propose a novel stepwise monotonic attention method in sequence-to-sequence acoustic modeling to improve the robustness on out-of-domain inputs.
Modeling Multi-speaker Latent Space to Improve Neural TTS: Quick Enrolling New Speaker and Enhancing Premium Voice
Neural TTS has shown it can generate high quality synthesized speech.
A Riemannian Framework for Matching Point Clouds Represented by the Schrodinger Distance Transform
In this paper, we use the well known Riemannian framework never before used for point cloud matching, and present a novel matching algorithm.
