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Found 10 papers, 0 papers with code
Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control
In this paper, a text-to-rapping/singing system is introduced, which can be adapted to any speaker's voice.
Cross-lingual Low Resource Speaker Adaptation Using Phonological Features
Subsequently, we fine-tune the model with very limited data of a new speaker's voice in either a seen or an unseen language, and achieve synthetic speech of equal quality, while preserving the target speaker's identity.
High Quality Streaming Speech Synthesis with Low, Sentence-Length-Independent Latency
This paper presents an end-to-end text-to-speech system with low latency on a CPU, suitable for real-time applications.
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.
Cross-lingual Text-To-Speech with Flow-based Voice Conversion for Improved Pronunciation
When used in a cross-lingual setting, acoustic features are initially produced with a native speaker of the target language and then voice conversion is applied by the same model in order to convert these features to the target speaker's voice.
Learning utterance-level representations through token-level acoustic latents prediction for Expressive Speech Synthesis
We show that the fine-grained latent space also captures coarse-grained information, which is more evident as the dimension of latent space increases in order to capture diverse prosodic representations.
Generating Multilingual Gender-Ambiguous Text-to-Speech Voices
The gender of any voice user interface is a key element of its perceived identity.
Investigating Content-Aware Neural Text-To-Speech MOS Prediction Using Prosodic and Linguistic Features
In modern high-quality neural TTS systems, prosodic appropriateness with regard to the spoken content is a decisive factor for speech naturalness.
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.
