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Found 8 papers, 0 papers with code
Expressive Machine Dubbing Through Phrase-level Cross-lingual Prosody Transfer
Speech generation for machine dubbing adds complexity to conventional Text-To-Speech solutions as the generated output is required to match the expressiveness, emotion and speaking rate of the source content.
On granularity of prosodic representations in expressive text-to-speech
In expressive speech synthesis it is widely adopted to use latent prosody representations to deal with variability of the data during training.
Enhancing audio quality for expressive Neural Text-to-Speech
Artificial speech synthesis has made a great leap in terms of naturalness as recent Text-to-Speech (TTS) systems are capable of producing speech with similar quality to human recordings.
Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speech
In this paper, we present a method for building highly expressive TTS voices with as little as 15 minutes of speech data from the target speaker.
Improving the expressiveness of neural vocoding with non-affine Normalizing Flows
In the waveform reconstruction task, the proposed model closes the naturalness and signal quality gap from the original PW to recordings by $10\%$, and from other state-of-the-art neural vocoding systems by more than $60\%$.
Universal Neural Vocoding with Parallel WaveNet
We present a universal neural vocoder based on Parallel WaveNet, with an additional conditioning network called Audio Encoder.
Fine-grained robust prosody transfer for single-speaker neural text-to-speech
However, when trained on a single-speaker dataset, the conventional prosody transfer systems are not robust enough to speaker variability, especially in the case of a reference signal coming from an unseen speaker.
Traditional Machine Learning for Pitch Detection
In this paper, we consider voicing detection as a classification problem and F0 contour estimation as a regression problem.
