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Found 13 papers, 3 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.
Controllable Emphasis with zero data for text-to-speech
We show that this technique significantly closes the gap to methods that require explicit recordings.
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.
Distribution augmentation for low-resource expressive text-to-speech
This paper presents a novel data augmentation technique for text-to-speech (TTS), that allows to generate new (text, audio) training examples without requiring any additional data.
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.
Gradient tree boosting with random output projections for multi-label classification and multi-output regression
We consider several extensions of gradient boosting to address such problems.
Globally Induced Forest: A Prepruning Compression Scheme
Tree-based ensemble models are heavy memory-wise.
Exploiting random projections and sparsity with random forests and gradient boosting methods -- Application to multi-label and multi-output learning, random forest model compression and leveraging input sparsity
Within machine learning, the supervised learning field aims at modeling the input-output relationship of a system, from past observations of its behavior.
Simple connectome inference from partial correlation statistics in calcium imaging
In this work, we propose a simple yet effective solution to the problem of connectome inference in calcium imaging data.
Random forests with random projections of the output space for high dimensional multi-label classification
We adapt the idea of random projections applied to the output space, so as to enhance tree-based ensemble methods in the context of multi-label classification.
API design for machine learning software: experiences from the scikit-learn project
Scikit-learn is an increasingly popular machine learning li- brary.
