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Found 11 papers, 1 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.
Comparing normalizing flows and diffusion models for prosody and acoustic modelling in text-to-speech
Neural text-to-speech systems are often optimized on L1/L2 losses, which make strong assumptions about the distributions of the target data space.
Controllable Emphasis with zero data for text-to-speech
We show that this technique significantly closes the gap to methods that require explicit recordings.
eCat: An End-to-End Model for Multi-Speaker TTS & Many-to-Many Fine-Grained Prosody Transfer
We show that eCat statistically significantly reduces the gap in naturalness between CopyCat2 and human recordings by an average of 46. 7% across 2 languages, 3 locales, and 7 speakers, along with better target-speaker similarity in FPT.
Simple and Effective Multi-sentence TTS with Expressive and Coherent Prosody
In this paper, we examine simple extensions to a Transformer-based FastSpeech-like system, with the goal of improving prosody for multi-sentence TTS.
Expressive, Variable, and Controllable Duration Modelling in TTS
First, we propose a duration model conditioned on phrasing that improves the predicted durations and provides better modelling of pauses.
CopyCat2: A Single Model for Multi-Speaker TTS and Many-to-Many Fine-Grained Prosody Transfer
In this paper, we present CopyCat2 (CC2), a novel model capable of: a) synthesizing speech with different speaker identities, b) generating speech with expressive and contextually appropriate prosody, and c) transferring prosody at fine-grained level between any pair of seen speakers.
Multi-Scale Spectrogram Modelling for Neural Text-to-Speech
We propose a novel Multi-Scale Spectrogram (MSS) modelling approach to synthesise speech with an improved coarse and fine-grained prosody.
A learned conditional prior for the VAE acoustic space of a TTS system
Many factors influence speech yielding different renditions of a given sentence.
Prosodic Representation Learning and Contextual Sampling for Neural Text-to-Speech
In Stage II, we propose a novel method to sample from this learnt prosodic distribution using the contextual information available in text.
A Geometric Approach to Obtain a Bird's Eye View from an Image
The objective of this paper is to rectify any monocular image by computing a homography matrix that transforms it to a bird's eye (overhead) view.
