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Found 6 papers, 2 papers with code
BASE TTS: Lessons from building a billion-parameter Text-to-Speech model on 100K hours of data
Echoing the widely-reported "emergent abilities" of large language models when trained on increasing volume of data, we show that BASE TTS variants built with 10K+ hours and 500M+ parameters begin to demonstrate natural prosody on textually complex sentences.
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.
Universal Neural Vocoding with Parallel WaveNet
We present a universal neural vocoder based on Parallel WaveNet, with an additional conditioning network called Audio Encoder.
Towards achieving robust universal neural vocoding
This vocoder is shown to be capable of generating speech of consistently good quality (98% relative mean MUSHRA when compared to natural speech) regardless of whether the input spectrogram comes from a speaker or style seen during training or from an out-of-domain scenario when the recording conditions are studio-quality.
Robust universal neural vocoding
This paper introduces a robust universal neural vocoder trained with 74 speakers (comprised of both genders) coming from 17 languages.
