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Found 8 papers, 0 papers with code
Improving the quality of neural TTS using long-form content and multi-speaker multi-style modeling
Neural text-to-speech (TTS) can provide quality close to natural speech if an adequate amount of high-quality speech material is available for training.
Vocal effort modeling in neural TTS for improving the intelligibility of synthetic speech in noise
We present a neural text-to-speech (TTS) method that models natural vocal effort variation to improve the intelligibility of synthetic speech in the presence of noise.
Hierarchical prosody modeling and control in non-autoregressive parallel neural TTS
Neural text-to-speech (TTS) synthesis can generate speech that is indistinguishable from natural speech.
Emphasis control for parallel neural TTS
Recent parallel neural text-to-speech (TTS) synthesis methods are able to generate speech with high fidelity while maintaining high performance.
On-device neural speech synthesis
Recent advances in text-to-speech (TTS) synthesis, such as Tacotron and WaveRNN, have made it possible to construct a fully neural network based TTS system, by coupling the two components together.
Whispered and Lombard Neural Speech Synthesis
2) Although our speaker verification (SV) model is not explicitly trained to discriminate different speaking styles, and no Lombard and whisper voice is used for pre-training this system, the SV model can be used as a style encoder for generating different style embeddings as input for the Tacotron system.
Controllable neural text-to-speech synthesis using intuitive prosodic features
Modern neural text-to-speech (TTS) synthesis can generate speech that is indistinguishable from natural speech.
Parametric Representation for Singing Voice Synthesis: a Comparative Evaluation
Various parametric representations have been proposed to model the speech signal.
